Cancer_Genomics_Final_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kthuang20/CancerGenomics/blob/main/Cancer_Genomics_Final_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Please click the above button or the below link to run and view the figures generated from this code notebook in Google Colab:**\n",
        "\n",
        "https://colab.research.google.com/gist/kthuang20/414210c00eaaa4cc01a837d861cb3457/cancer-genomics-final-project.ipynb"
      ],
      "metadata": {
        "id": "HnoZkmNIy124"
      },
      "id": "HnoZkmNIy124"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing necessary packages"
      ],
      "metadata": {
        "id": "uFop03FW6xi3"
      },
      "id": "uFop03FW6xi3"
    },
    {
      "cell_type": "code",
      "source": [
        "# import the necessary files used in this notebook\n",
        "! gdown --id 1oNNQMVu9FZ-Lg_q26S-cjLGK80Y-emoD # mRNA seq\n",
        "! gdown --id 1FpKBwcRz1ujdil4IaiNlmAjFdb0PZNjd # methylation\n",
        "! gdown --id 1LOCgSeKXS-GPgZIff3qA4VhhFKMPDOpt # CNA\n",
        "! gdown --id 1B1b_2qiFPtnLviL4npRLHNLpkWz1jFAI # gene list\n",
        "! gdown --id 1jZyU7nMRPMVLJmg02nZ14BQA6_mVWgod # mutations\n",
        "! gdown --id 1EU9aIaCcm-qB1jEe8hK25bgRCdhIefiS # survival outcomes\n",
        "! gdown --id 1NEJG2KPBkEssCJ-kbtcbWchweJ7ZPkD0 # log2(CNA)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6275eZgztEm1",
        "outputId": "b3feb86a-b539-4f10-8949-44d764a0a322"
      },
      "id": "6275eZgztEm1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oNNQMVu9FZ-Lg_q26S-cjLGK80Y-emoD\n",
            "To: /content/data_mrna_seq_v2_rsem_zscores_ref_all_samples.txt\n",
            "100% 26.8M/26.8M [00:01<00:00, 23.1MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1FpKBwcRz1ujdil4IaiNlmAjFdb0PZNjd\n",
            "To: /content/data_methylation_hm27_hm450_merged.txt\n",
            "100% 77.3M/77.3M [00:03<00:00, 23.5MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1LOCgSeKXS-GPgZIff3qA4VhhFKMPDOpt\n",
            "To: /content/data_cna.txt\n",
            "100% 10.3M/10.3M [00:00<00:00, 44.6MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1B1b_2qiFPtnLviL4npRLHNLpkWz1jFAI\n",
            "To: /content/wnt_gene_list.txt\n",
            "100% 1.64k/1.64k [00:00<00:00, 8.04MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1jZyU7nMRPMVLJmg02nZ14BQA6_mVWgod\n",
            "To: /content/data_mutations.txt\n",
            "100% 34.3M/34.3M [00:01<00:00, 28.5MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1EU9aIaCcm-qB1jEe8hK25bgRCdhIefiS\n",
            "To: /content/data_clinical_patient.txt\n",
            "100% 58.6k/58.6k [00:00<00:00, 92.6MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1NEJG2KPBkEssCJ-kbtcbWchweJ7ZPkD0\n",
            "To: /content/data_log2_cna.txt\n",
            "100% 28.6M/28.6M [00:00<00:00, 56.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install dash-bio\n",
        "!pip install dash-bio"
      ],
      "metadata": {
        "id": "bBYvDH3crlmb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d4a5e10-835b-46f8-addc-d793d2a9c7e4"
      },
      "id": "bBYvDH3crlmb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: dash-bio, parmed, periodictable\n",
            "  Building wheel for dash-bio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/cb/97/9c/49463914611cb715a46d21a3896d9a886318ea0c451e840196\n",
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            "  Created wheel for parmed: filename=ParmEd-4.2.2-cp310-cp310-linux_x86_64.whl size=19468084 sha256=4b3e408c1ce6228635d6cb337acb7de472d2f7f4a90ddfe849273a8b8ca40d97\n",
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            "  Created wheel for periodictable: filename=periodictable-1.7.0-py3-none-any.whl size=752513 sha256=1fb2296aa8e62d20169f7846133018f778d0d199ae71ab70f8761b6339ccf2ec\n",
            "  Stored in directory: /root/.cache/pip/wheels/7e/19/a2/fef5d0ca2b1ad2b199e863a6e796ad9d5efc86563d80c91a0c\n",
            "Successfully built dash-bio parmed periodictable\n",
            "Installing collected packages: dash-table, dash-html-components, dash-core-components, retrying, periodictable, parmed, biopython, GEOparse, dash, dash-bio\n",
            "Successfully installed GEOparse-2.0.4 biopython-1.83 dash-2.16.1 dash-bio-1.0.2 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 parmed-4.2.2 periodictable-1.7.0 retrying-1.3.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d9eaa48-66ac-4d20-b917-e93b8a2115c5",
      "metadata": {
        "id": "1d9eaa48-66ac-4d20-b917-e93b8a2115c5"
      },
      "outputs": [],
      "source": [
        "# import necessary packages\n",
        "import pandas as pd\n",
        "from scipy.stats import fisher_exact\n",
        "import plotly.express as px\n",
        "import dash_bio"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feedback\n",
        "\n",
        "*Comments from draft:*\n",
        "* The figure in Q5 did not provide a visualization to show that there was co-occurence or mutual exclusivity. I have redone this heatmap to show the mutation frequencies across all patients for the top 3 mutated genes to show this.\n",
        "* I also did not explain how the figure in Q5 shows co-occurence so I have revised the explanation to describe how my visualization shows that mutations in the the genes co-occur.\n",
        "* The explanation of how the pathway relates to this paper was unclear so I have added an additional sentence stating that I found SMAD4 to be one of the top mutated genes in the Wnt pathway, which is what the paper also found.\n",
        "\n",
        "*Comments from my peer review:*\n",
        "* One peer mentioned that the figures are not visible on the GitHub. Please click on the link to the Google Colab to view the figures.\n",
        "* The description of how I generated the heatmap was confusing so I have included an additional paragraph before the figure describing how I performed a Fisher's Exact Test to calculated the values used to generate the heatmap.\n",
        "\n",
        "*Comments from the outline:*\n",
        "* My figures were not visible so I have uploaded it onto Google Colab so that anyone may access and run the code notebook.\n",
        "* My initial figure in Q5 visualizing co-occurence and mutual exclusivity was incorrect. My graphs were comparing the DNA expression between genes rather comparing the pattern in mutations between pairs of genes. I have redid my analysis for these figures by using a Fisher's Exact Test to determine whether there is an associated between the mutations in any pair of genes and generated a heatmap to visualize these results.\n",
        "* My initial hypothesis in Q6 was vague and so I created a different hypothesis and tried to be more specific. I also explained what I would expect to see in my figure if hypothesis was supported."
      ],
      "metadata": {
        "id": "SFiaNruhrByf"
      },
      "id": "SFiaNruhrByf"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploring the Genetic Effects of Wnt Pathway on Pancreatic Adenocarcinoma"
      ],
      "metadata": {
        "id": "DxPxFow76jXY"
      },
      "id": "DxPxFow76jXY"
    },
    {
      "cell_type": "markdown",
      "id": "afb5df4c-d250-42d7-8eef-6cb23a501e75",
      "metadata": {
        "id": "afb5df4c-d250-42d7-8eef-6cb23a501e75"
      },
      "source": [
        "### 1. Pancreatic Adenocarcinoma and Wnt Pathway\n",
        "*a. Using the CDC website, research the number of people who develop this cancer in the USA and other relevant information*\n",
        "\n",
        "> In 2020, there were 52,747 new cases of pancreatic cancer and 46,774 deaths from it. In other words, for every 100,000 people, 13 new cases of pancreatic cancer were found and 11 people died from it [[1]](https://gis.cdc.gov/Cancer/USCS/#/AtAGlance/). Pancreatic adenocarcinoma is the most common type of cancer, accounting for 95% of exocrine pancreatic cancer, and therefore will be the focus of this project [[2]](https://www.cancer.org/cancer/types/pancreatic-cancer/about/what-is-pancreatic-cancer.html).\n",
        "\n",
        "*b. Describe how your pathway can impact cancer.*\n",
        "\n",
        "> In this pathway's inactivated state, β-catenin protein is normally broken down and degraded through the ubiquitin-proteasomal pathway, keeping levels of β-catenin levels low. This keeps transcription factors inhibited from binding to the DNA to transcribe genes associated with cell proliferation and growth. However, when this pathway is activated, Wnt acts to prevent β-catenin from being degraded, increasing β-catenin levels. β-catenin can then bind to the transcription factors, promoting the transcription of these genes [[3]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357762/).\n",
        ">\n",
        "> Therefore, mutations that lead to sustained activation of the pathway or prevent the degradation of β-catenin can result in increased levels of β-catenin. This excess β-catenin can then bind to transcription factors and promote the transcription of genes involved in cell proliferation and growth, ultimately leading to uncontrolled cell growth and cancer."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c87a6101-2da9-47b4-9693-b2a7f3e9cbd7",
      "metadata": {
        "id": "c87a6101-2da9-47b4-9693-b2a7f3e9cbd7"
      },
      "source": [
        "### 2. Pancreatic Adencarcinoma PanCancer Atlas Dataset\n",
        "What are the **observations** and what are the **variables** for each observation? How many are there of each?\n",
        "> This project uses TCGA data on Pancreatic Adenocarcinoma generated by the PanCancer Atlas [[4]](https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas_2018). The dataset contains a total of 184 tumor samples (observations). The following was measured from the samples:\n",
        "> * mRNA expression levels of each of 19,893 genes (measured for 177/184 samples)\n",
        "> * methlyation levels of 22,601 genomic regions (measured for all 184 samples)\n",
        "> * the mutations of each of the 184 samples in each gene (if any)\n",
        "> * copy number alterations in each gene (done for 183/184 samples)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1521cc4d-c0ac-4b4c-bb92-751259bca84b",
      "metadata": {
        "id": "1521cc4d-c0ac-4b4c-bb92-751259bca84b",
        "outputId": "1dd85ccc-b2dc-41df-8608-58f7422a40af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hugo_Symbol  Entrez_Gene_Id  TCGA-2J-AAB1-01  TCGA-2J-AAB4-01  \\\n",
              "2        UBE2Q2P2       100134869           0.0096           0.0599   \n",
              "3         HMGB1P1           10357           0.3928          -0.5735   \n",
              "7        RNU12-2P           26823          -0.8900          -0.1096   \n",
              "10          EZHIP          340602          -0.7417          -1.4532   \n",
              "11         EFCAB8          388795          -0.8963          -0.9661   \n",
              "...           ...             ...              ...              ...   \n",
              "20526      ZYG11A          440590          -1.4824          -1.4824   \n",
              "20527      ZYG11B           79699          -0.4936          -0.1143   \n",
              "20528         ZYX            7791           0.8177           0.1063   \n",
              "20529       ZZEF1           23140          -0.1629          -0.6069   \n",
              "20530        ZZZ3           26009           0.1529          -0.6126   \n",
              "\n",
              "       TCGA-2J-AAB6-01  TCGA-2J-AAB8-01  TCGA-2J-AAB9-01  TCGA-2J-AABA-01  \\\n",
              "2              -0.9737           0.8455           0.8184           1.7167   \n",
              "3               1.8915          -0.0921          -0.4910          -1.1825   \n",
              "7              -2.3425          -2.3425          -2.3425          -0.5646   \n",
              "10             -1.4532          -1.4532           0.5981          -1.4532   \n",
              "11              0.7841          -1.4985           1.3052          -0.2677   \n",
              "...                ...              ...              ...              ...   \n",
              "20526          -0.7256           1.0819          -0.2853          -0.1478   \n",
              "20527           0.0619           0.0479          -0.1151           1.0851   \n",
              "20528           1.7172           0.2075           0.9191          -0.2003   \n",
              "20529          -0.7065          -0.8278          -0.1500           0.4317   \n",
              "20530           0.0742           0.1151          -0.0869           0.9984   \n",
              "\n",
              "       TCGA-2J-AABE-01  TCGA-2J-AABF-01  ...  TCGA-XD-AAUG-01  \\\n",
              "2               1.3022           0.6549  ...           1.0778   \n",
              "3               0.1399           0.6376  ...          -1.4345   \n",
              "7              -2.3425          -2.3425  ...           0.7834   \n",
              "10             -1.4532          -0.1561  ...           1.6732   \n",
              "11             -1.4985           0.5388  ...           0.1802   \n",
              "...                ...              ...  ...              ...   \n",
              "20526          -0.6930          -0.2921  ...          -1.4824   \n",
              "20527          -0.5433           0.2243  ...           0.5972   \n",
              "20528           0.7436           0.3015  ...           0.8103   \n",
              "20529          -0.8149           0.7066  ...          -0.0721   \n",
              "20530          -0.7608           1.2720  ...           0.3145   \n",
              "\n",
              "       TCGA-XD-AAUH-01  TCGA-XD-AAUI-01  TCGA-XD-AAUL-01  TCGA-XN-A8T3-01  \\\n",
              "2              -0.6572           0.2441           0.6851          -0.2044   \n",
              "3              -0.6339          -0.3799           0.9538           1.1697   \n",
              "7               0.2660           0.1744          -2.3425           1.8053   \n",
              "10              0.5628          -0.2203          -1.4532          -0.1633   \n",
              "11             -0.4171           1.2052          -0.5812          -0.8565   \n",
              "...                ...              ...              ...              ...   \n",
              "20526          -1.4824           0.9774          -1.4824          -0.7863   \n",
              "20527           0.6609          -0.3349           0.1784           0.2845   \n",
              "20528           0.0656           0.3420           1.7124           0.2205   \n",
              "20529           0.6847          -0.3864          -0.5566           0.2326   \n",
              "20530           0.8818          -0.4968          -0.5072           0.6488   \n",
              "\n",
              "       TCGA-XN-A8T5-01  TCGA-YB-A89D-01  TCGA-YH-A8SY-01  TCGA-YY-A8LH-01  \\\n",
              "2               0.3293           0.0401          -0.8810           1.5454   \n",
              "3              -0.5930           0.2875           0.1249           0.6197   \n",
              "7              -0.6336          -2.3425          -2.3425          -0.0309   \n",
              "10              0.7300           0.5472          -0.6293          -0.3208   \n",
              "11              0.3490          -0.4271          -1.4985          -0.2211   \n",
              "...                ...              ...              ...              ...   \n",
              "20526           0.2026          -1.4824          -0.7264          -1.4824   \n",
              "20527           0.0807           0.3530          -0.3165          -2.3282   \n",
              "20528           0.5369           1.1194           1.2689          -0.0816   \n",
              "20529           0.3626          -0.4978          -0.6741          -0.9126   \n",
              "20530          -0.0530          -0.8723          -1.5048          -1.9241   \n",
              "\n",
              "       TCGA-Z5-AAPL-01  \n",
              "2               0.4204  \n",
              "3               1.2980  \n",
              "7               2.5850  \n",
              "10              2.9710  \n",
              "11              2.5727  \n",
              "...                ...  \n",
              "20526           2.2256  \n",
              "20527           0.1352  \n",
              "20528          -0.1359  \n",
              "20529           1.2289  \n",
              "20530           0.5563  \n",
              "\n",
              "[19893 rows x 179 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hugo_Symbol</th>\n",
              "      <th>Entrez_Gene_Id</th>\n",
              "      <th>TCGA-2J-AAB1-01</th>\n",
              "      <th>TCGA-2J-AAB4-01</th>\n",
              "      <th>TCGA-2J-AAB6-01</th>\n",
              "      <th>TCGA-2J-AAB8-01</th>\n",
              "      <th>TCGA-2J-AAB9-01</th>\n",
              "      <th>TCGA-2J-AABA-01</th>\n",
              "      <th>TCGA-2J-AABE-01</th>\n",
              "      <th>TCGA-2J-AABF-01</th>\n",
              "      <th>...</th>\n",
              "      <th>TCGA-XD-AAUG-01</th>\n",
              "      <th>TCGA-XD-AAUH-01</th>\n",
              "      <th>TCGA-XD-AAUI-01</th>\n",
              "      <th>TCGA-XD-AAUL-01</th>\n",
              "      <th>TCGA-XN-A8T3-01</th>\n",
              "      <th>TCGA-XN-A8T5-01</th>\n",
              "      <th>TCGA-YB-A89D-01</th>\n",
              "      <th>TCGA-YH-A8SY-01</th>\n",
              "      <th>TCGA-YY-A8LH-01</th>\n",
              "      <th>TCGA-Z5-AAPL-01</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>UBE2Q2P2</td>\n",
              "      <td>100134869</td>\n",
              "      <td>0.0096</td>\n",
              "      <td>0.0599</td>\n",
              "      <td>-0.9737</td>\n",
              "      <td>0.8455</td>\n",
              "      <td>0.8184</td>\n",
              "      <td>1.7167</td>\n",
              "      <td>1.3022</td>\n",
              "      <td>0.6549</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0778</td>\n",
              "      <td>-0.6572</td>\n",
              "      <td>0.2441</td>\n",
              "      <td>0.6851</td>\n",
              "      <td>-0.2044</td>\n",
              "      <td>0.3293</td>\n",
              "      <td>0.0401</td>\n",
              "      <td>-0.8810</td>\n",
              "      <td>1.5454</td>\n",
              "      <td>0.4204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>HMGB1P1</td>\n",
              "      <td>10357</td>\n",
              "      <td>0.3928</td>\n",
              "      <td>-0.5735</td>\n",
              "      <td>1.8915</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>-0.4910</td>\n",
              "      <td>-1.1825</td>\n",
              "      <td>0.1399</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.4345</td>\n",
              "      <td>-0.6339</td>\n",
              "      <td>-0.3799</td>\n",
              "      <td>0.9538</td>\n",
              "      <td>1.1697</td>\n",
              "      <td>-0.5930</td>\n",
              "      <td>0.2875</td>\n",
              "      <td>0.1249</td>\n",
              "      <td>0.6197</td>\n",
              "      <td>1.2980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RNU12-2P</td>\n",
              "      <td>26823</td>\n",
              "      <td>-0.8900</td>\n",
              "      <td>-0.1096</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-0.5646</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7834</td>\n",
              "      <td>0.2660</td>\n",
              "      <td>0.1744</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>1.8053</td>\n",
              "      <td>-0.6336</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-2.3425</td>\n",
              "      <td>-0.0309</td>\n",
              "      <td>2.5850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EZHIP</td>\n",
              "      <td>340602</td>\n",
              "      <td>-0.7417</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>0.5981</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>-0.1561</td>\n",
              "      <td>...</td>\n",
              "      <td>1.6732</td>\n",
              "      <td>0.5628</td>\n",
              "      <td>-0.2203</td>\n",
              "      <td>-1.4532</td>\n",
              "      <td>-0.1633</td>\n",
              "      <td>0.7300</td>\n",
              "      <td>0.5472</td>\n",
              "      <td>-0.6293</td>\n",
              "      <td>-0.3208</td>\n",
              "      <td>2.9710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>EFCAB8</td>\n",
              "      <td>388795</td>\n",
              "      <td>-0.8963</td>\n",
              "      <td>-0.9661</td>\n",
              "      <td>0.7841</td>\n",
              "      <td>-1.4985</td>\n",
              "      <td>1.3052</td>\n",
              "      <td>-0.2677</td>\n",
              "      <td>-1.4985</td>\n",
              "      <td>0.5388</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1802</td>\n",
              "      <td>-0.4171</td>\n",
              "      <td>1.2052</td>\n",
              "      <td>-0.5812</td>\n",
              "      <td>-0.8565</td>\n",
              "      <td>0.3490</td>\n",
              "      <td>-0.4271</td>\n",
              "      <td>-1.4985</td>\n",
              "      <td>-0.2211</td>\n",
              "      <td>2.5727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>20526</th>\n",
              "      <td>ZYG11A</td>\n",
              "      <td>440590</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>-0.7256</td>\n",
              "      <td>1.0819</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>-0.1478</td>\n",
              "      <td>-0.6930</td>\n",
              "      <td>-0.2921</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>0.9774</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>-0.7863</td>\n",
              "      <td>0.2026</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>-0.7264</td>\n",
              "      <td>-1.4824</td>\n",
              "      <td>2.2256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20527</th>\n",
              "      <td>ZYG11B</td>\n",
              "      <td>79699</td>\n",
              "      <td>-0.4936</td>\n",
              "      <td>-0.1143</td>\n",
              "      <td>0.0619</td>\n",
              "      <td>0.0479</td>\n",
              "      <td>-0.1151</td>\n",
              "      <td>1.0851</td>\n",
              "      <td>-0.5433</td>\n",
              "      <td>0.2243</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5972</td>\n",
              "      <td>0.6609</td>\n",
              "      <td>-0.3349</td>\n",
              "      <td>0.1784</td>\n",
              "      <td>0.2845</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>0.3530</td>\n",
              "      <td>-0.3165</td>\n",
              "      <td>-2.3282</td>\n",
              "      <td>0.1352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20528</th>\n",
              "      <td>ZYX</td>\n",
              "      <td>7791</td>\n",
              "      <td>0.8177</td>\n",
              "      <td>0.1063</td>\n",
              "      <td>1.7172</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>0.9191</td>\n",
              "      <td>-0.2003</td>\n",
              "      <td>0.7436</td>\n",
              "      <td>0.3015</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8103</td>\n",
              "      <td>0.0656</td>\n",
              "      <td>0.3420</td>\n",
              "      <td>1.7124</td>\n",
              "      <td>0.2205</td>\n",
              "      <td>0.5369</td>\n",
              "      <td>1.1194</td>\n",
              "      <td>1.2689</td>\n",
              "      <td>-0.0816</td>\n",
              "      <td>-0.1359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20529</th>\n",
              "      <td>ZZEF1</td>\n",
              "      <td>23140</td>\n",
              "      <td>-0.1629</td>\n",
              "      <td>-0.6069</td>\n",
              "      <td>-0.7065</td>\n",
              "      <td>-0.8278</td>\n",
              "      <td>-0.1500</td>\n",
              "      <td>0.4317</td>\n",
              "      <td>-0.8149</td>\n",
              "      <td>0.7066</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0721</td>\n",
              "      <td>0.6847</td>\n",
              "      <td>-0.3864</td>\n",
              "      <td>-0.5566</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.3626</td>\n",
              "      <td>-0.4978</td>\n",
              "      <td>-0.6741</td>\n",
              "      <td>-0.9126</td>\n",
              "      <td>1.2289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20530</th>\n",
              "      <td>ZZZ3</td>\n",
              "      <td>26009</td>\n",
              "      <td>0.1529</td>\n",
              "      <td>-0.6126</td>\n",
              "      <td>0.0742</td>\n",
              "      <td>0.1151</td>\n",
              "      <td>-0.0869</td>\n",
              "      <td>0.9984</td>\n",
              "      <td>-0.7608</td>\n",
              "      <td>1.2720</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3145</td>\n",
              "      <td>0.8818</td>\n",
              "      <td>-0.4968</td>\n",
              "      <td>-0.5072</td>\n",
              "      <td>0.6488</td>\n",
              "      <td>-0.0530</td>\n",
              "      <td>-0.8723</td>\n",
              "      <td>-1.5048</td>\n",
              "      <td>-1.9241</td>\n",
              "      <td>0.5563</td>\n",
              "    </tr>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        google.colab.notebook.generateWithVariable('mRNA_exp');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mRNA_exp"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# location of file containing the mRNA sequencing data for all samples\n",
        "file_path = 'data_mrna_seq_v2_rsem_zscores_ref_all_samples.txt'\n",
        "# import the dataset as a dataframe\n",
        "mRNA_exp = pd.read_table(file_path)\n",
        "# remove rows without any genes\n",
        "mRNA_exp = mRNA_exp[mRNA_exp['Hugo_Symbol'].notna()]\n",
        "# remove rows where there was no expression levels recorded\n",
        "mRNA_exp = mRNA_exp.loc[mRNA_exp.drop(columns=['Hugo_Symbol','Entrez_Gene_Id']).notna().sum(axis=1) > 0]\n",
        "mRNA_exp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "157a2223-8c44-4c33-92fb-440caeb2249c",
      "metadata": {
        "id": "157a2223-8c44-4c33-92fb-440caeb2249c",
        "outputId": "e1844b6a-5b51-402b-c113-fc090bb12b97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ENTITY_STABLE_ID            NAME    DESCRIPTION  \\\n",
              "0           cg00000292          ATP2A1        1stExon   \n",
              "1           cg00003994           MEOX2        1stExon   \n",
              "2           cg00005847           HOXD3          5'UTR   \n",
              "3           cg00007981           PANX1        1stExon   \n",
              "4           cg00008493  KIAA1409;COX8C     Body;5'UTR   \n",
              "...                ...             ...            ...   \n",
              "22596       cg27657283  LEPRE1;C1orf50   Body;TSS1500   \n",
              "22597       cg27661264            GNAS  TSS1500;3'UTR   \n",
              "22598       cg27662379            RPN1         TSS200   \n",
              "22599       cg27662877           CYB5A        TSS1500   \n",
              "22600       cg27665659           AP1S1         TSS200   \n",
              "\n",
              "                          TRANSCRIPT_ID  TCGA-2J-AAB1-01  TCGA-2J-AAB4-01  \\\n",
              "0                   NM_173201;NM_004320         0.733917         0.719068   \n",
              "1                             NM_005924         0.110034         0.112912   \n",
              "2                             NM_006898         0.598123         0.144939   \n",
              "3                             NM_015368         0.025716         0.025669   \n",
              "4                   NM_020818;NM_182971         0.947818         0.918361   \n",
              "...                                 ...              ...              ...   \n",
              "22596  NM_022356;NM_001146289;NM_024097         0.032062         0.033511   \n",
              "22597  NM_016592;NM_001077490;NM_080425         0.439556         0.224960   \n",
              "22598                         NM_002950         0.087215         0.045312   \n",
              "22599               NM_001914;NM_148923         0.033459         0.037676   \n",
              "22600                         NM_001283         0.067697         0.065232   \n",
              "\n",
              "       TCGA-2J-AAB6-01  TCGA-2J-AAB8-01  TCGA-2J-AAB9-01  TCGA-2J-AABA-01  \\\n",
              "0             0.560780         0.670286         0.688073         0.705372   \n",
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              "...                ...              ...              ...              ...   \n",
              "22596         0.030302         0.031422         0.044181         0.037770   \n",
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              "22599         0.056683         0.035521         0.042462         0.039009   \n",
              "22600         0.061940         0.089677         0.113031         0.065799   \n",
              "\n",
              "       ...  TCGA-XD-AAUG-01  TCGA-XD-AAUH-01  TCGA-XD-AAUI-01  \\\n",
              "0      ...         0.632310         0.761573         0.644253   \n",
              "1      ...         0.057942         0.071444         0.093027   \n",
              "2      ...         0.254899         0.373482         0.343299   \n",
              "3      ...         0.023778         0.020776         0.024543   \n",
              "4      ...         0.949865         0.944085         0.942790   \n",
              "...    ...              ...              ...              ...   \n",
              "22596  ...         0.032174         0.029996         0.029864   \n",
              "22597  ...         0.333729         0.309576         0.363753   \n",
              "22598  ...         0.066277         0.057452         0.099178   \n",
              "22599  ...         0.040657         0.027856         0.031584   \n",
              "22600  ...         0.116519         0.084938         0.072964   \n",
              "\n",
              "       TCGA-XD-AAUL-01  TCGA-XN-A8T3-01  TCGA-XN-A8T5-01  TCGA-YB-A89D-01  \\\n",
              "0             0.697230         0.550313         0.736484         0.678476   \n",
              "1             0.062793         0.066140         0.091232         0.056195   \n",
              "2             0.321652         0.356024         0.306038         0.400456   \n",
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              "4             0.919763         0.944443         0.933676         0.920512   \n",
              "...                ...              ...              ...              ...   \n",
              "22596         0.032304         0.033330         0.042130         0.041855   \n",
              "22597         0.319711         0.403687         0.420992         0.382321   \n",
              "22598         0.039511         0.037407         0.043382         0.054221   \n",
              "22599         0.027708         0.051507         0.038634         0.044226   \n",
              "22600         0.075596         0.111124         0.108508         0.108508   \n",
              "\n",
              "       TCGA-YH-A8SY-01  TCGA-YY-A8LH-01  TCGA-Z5-AAPL-01  \n",
              "0             0.443366         0.634037         0.841216  \n",
              "1             0.218264         0.215530         0.077785  \n",
              "2             0.549984         0.744128         0.292187  \n",
              "3             0.020904         0.031055         0.026724  \n",
              "4             0.934375         0.958107         0.940709  \n",
              "...                ...              ...              ...  \n",
              "22596         0.038142         0.038249         0.027484  \n",
              "22597         0.280691         0.448022         0.329237  \n",
              "22598         0.041047         0.043322         0.041996  \n",
              "22599         0.045798         0.048060         0.028841  \n",
              "22600         0.083195         0.090846         0.071829  \n",
              "\n",
              "[22601 rows x 187 columns]"
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              "      <th>ENTITY_STABLE_ID</th>\n",
              "      <th>NAME</th>\n",
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              "      <th>TCGA-2J-AAB1-01</th>\n",
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              "      <td>0.670286</td>\n",
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              "      <td>0.443366</td>\n",
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              "      <td>0.841216</td>\n",
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              "      <td>0.066140</td>\n",
              "      <td>0.091232</td>\n",
              "      <td>0.056195</td>\n",
              "      <td>0.218264</td>\n",
              "      <td>0.215530</td>\n",
              "      <td>0.077785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>cg00005847</td>\n",
              "      <td>HOXD3</td>\n",
              "      <td>5'UTR</td>\n",
              "      <td>NM_006898</td>\n",
              "      <td>0.598123</td>\n",
              "      <td>0.144939</td>\n",
              "      <td>0.471551</td>\n",
              "      <td>0.334088</td>\n",
              "      <td>0.271976</td>\n",
              "      <td>0.497987</td>\n",
              "      <td>...</td>\n",
              "      <td>0.254899</td>\n",
              "      <td>0.373482</td>\n",
              "      <td>0.343299</td>\n",
              "      <td>0.321652</td>\n",
              "      <td>0.356024</td>\n",
              "      <td>0.306038</td>\n",
              "      <td>0.400456</td>\n",
              "      <td>0.549984</td>\n",
              "      <td>0.744128</td>\n",
              "      <td>0.292187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cg00007981</td>\n",
              "      <td>PANX1</td>\n",
              "      <td>1stExon</td>\n",
              "      <td>NM_015368</td>\n",
              "      <td>0.025716</td>\n",
              "      <td>0.025669</td>\n",
              "      <td>0.026010</td>\n",
              "      <td>0.025039</td>\n",
              "      <td>0.031081</td>\n",
              "      <td>0.027746</td>\n",
              "      <td>...</td>\n",
              "      <td>0.023778</td>\n",
              "      <td>0.020776</td>\n",
              "      <td>0.024543</td>\n",
              "      <td>0.021864</td>\n",
              "      <td>0.026096</td>\n",
              "      <td>0.031424</td>\n",
              "      <td>0.029742</td>\n",
              "      <td>0.020904</td>\n",
              "      <td>0.031055</td>\n",
              "      <td>0.026724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cg00008493</td>\n",
              "      <td>KIAA1409;COX8C</td>\n",
              "      <td>Body;5'UTR</td>\n",
              "      <td>NM_020818;NM_182971</td>\n",
              "      <td>0.947818</td>\n",
              "      <td>0.918361</td>\n",
              "      <td>0.937682</td>\n",
              "      <td>0.934786</td>\n",
              "      <td>0.938346</td>\n",
              "      <td>0.943597</td>\n",
              "      <td>...</td>\n",
              "      <td>0.949865</td>\n",
              "      <td>0.944085</td>\n",
              "      <td>0.942790</td>\n",
              "      <td>0.919763</td>\n",
              "      <td>0.944443</td>\n",
              "      <td>0.933676</td>\n",
              "      <td>0.920512</td>\n",
              "      <td>0.934375</td>\n",
              "      <td>0.958107</td>\n",
              "      <td>0.940709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22596</th>\n",
              "      <td>cg27657283</td>\n",
              "      <td>LEPRE1;C1orf50</td>\n",
              "      <td>Body;TSS1500</td>\n",
              "      <td>NM_022356;NM_001146289;NM_024097</td>\n",
              "      <td>0.032062</td>\n",
              "      <td>0.033511</td>\n",
              "      <td>0.030302</td>\n",
              "      <td>0.031422</td>\n",
              "      <td>0.044181</td>\n",
              "      <td>0.037770</td>\n",
              "      <td>...</td>\n",
              "      <td>0.032174</td>\n",
              "      <td>0.029996</td>\n",
              "      <td>0.029864</td>\n",
              "      <td>0.032304</td>\n",
              "      <td>0.033330</td>\n",
              "      <td>0.042130</td>\n",
              "      <td>0.041855</td>\n",
              "      <td>0.038142</td>\n",
              "      <td>0.038249</td>\n",
              "      <td>0.027484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22597</th>\n",
              "      <td>cg27661264</td>\n",
              "      <td>GNAS</td>\n",
              "      <td>TSS1500;3'UTR</td>\n",
              "      <td>NM_016592;NM_001077490;NM_080425</td>\n",
              "      <td>0.439556</td>\n",
              "      <td>0.224960</td>\n",
              "      <td>0.335165</td>\n",
              "      <td>0.381820</td>\n",
              "      <td>0.312923</td>\n",
              "      <td>0.399568</td>\n",
              "      <td>...</td>\n",
              "      <td>0.333729</td>\n",
              "      <td>0.309576</td>\n",
              "      <td>0.363753</td>\n",
              "      <td>0.319711</td>\n",
              "      <td>0.403687</td>\n",
              "      <td>0.420992</td>\n",
              "      <td>0.382321</td>\n",
              "      <td>0.280691</td>\n",
              "      <td>0.448022</td>\n",
              "      <td>0.329237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22598</th>\n",
              "      <td>cg27662379</td>\n",
              "      <td>RPN1</td>\n",
              "      <td>TSS200</td>\n",
              "      <td>NM_002950</td>\n",
              "      <td>0.087215</td>\n",
              "      <td>0.045312</td>\n",
              "      <td>0.084045</td>\n",
              "      <td>0.075204</td>\n",
              "      <td>0.117227</td>\n",
              "      <td>0.057109</td>\n",
              "      <td>...</td>\n",
              "      <td>0.066277</td>\n",
              "      <td>0.057452</td>\n",
              "      <td>0.099178</td>\n",
              "      <td>0.039511</td>\n",
              "      <td>0.037407</td>\n",
              "      <td>0.043382</td>\n",
              "      <td>0.054221</td>\n",
              "      <td>0.041047</td>\n",
              "      <td>0.043322</td>\n",
              "      <td>0.041996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22599</th>\n",
              "      <td>cg27662877</td>\n",
              "      <td>CYB5A</td>\n",
              "      <td>TSS1500</td>\n",
              "      <td>NM_001914;NM_148923</td>\n",
              "      <td>0.033459</td>\n",
              "      <td>0.037676</td>\n",
              "      <td>0.056683</td>\n",
              "      <td>0.035521</td>\n",
              "      <td>0.042462</td>\n",
              "      <td>0.039009</td>\n",
              "      <td>...</td>\n",
              "      <td>0.040657</td>\n",
              "      <td>0.027856</td>\n",
              "      <td>0.031584</td>\n",
              "      <td>0.027708</td>\n",
              "      <td>0.051507</td>\n",
              "      <td>0.038634</td>\n",
              "      <td>0.044226</td>\n",
              "      <td>0.045798</td>\n",
              "      <td>0.048060</td>\n",
              "      <td>0.028841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22600</th>\n",
              "      <td>cg27665659</td>\n",
              "      <td>AP1S1</td>\n",
              "      <td>TSS200</td>\n",
              "      <td>NM_001283</td>\n",
              "      <td>0.067697</td>\n",
              "      <td>0.065232</td>\n",
              "      <td>0.061940</td>\n",
              "      <td>0.089677</td>\n",
              "      <td>0.113031</td>\n",
              "      <td>0.065799</td>\n",
              "      <td>...</td>\n",
              "      <td>0.116519</td>\n",
              "      <td>0.084938</td>\n",
              "      <td>0.072964</td>\n",
              "      <td>0.075596</td>\n",
              "      <td>0.111124</td>\n",
              "      <td>0.108508</td>\n",
              "      <td>0.108508</td>\n",
              "      <td>0.083195</td>\n",
              "      <td>0.090846</td>\n",
              "      <td>0.071829</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22601 rows × 187 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('methylation_levels');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "methylation_levels"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# location of file containing the methylation of a genomic region of an exome\n",
        "file_path = 'data_methylation_hm27_hm450_merged.txt'\n",
        "# import the dataset as a dataframe\n",
        "methylation_levels = pd.read_table(file_path)\n",
        "methylation_levels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50373aa4-cc41-4709-b0bf-0c71901c9bb9",
      "metadata": {
        "id": "50373aa4-cc41-4709-b0bf-0c71901c9bb9",
        "outputId": "a297206a-5f3d-44c6-e4c8-487593f1e92b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Hugo_Symbol  Entrez_Gene_Id  TCGA-2J-AAB1-01  TCGA-2J-AAB4-01  \\\n",
              "0               ACAP3        116983.0                0                0   \n",
              "1              ACTRT2        140625.0                0                0   \n",
              "2                AGRN        375790.0                0                0   \n",
              "3             ANKRD65        441869.0                0                0   \n",
              "4              ATAD3A         55210.0                0                0   \n",
              "...               ...             ...              ...              ...   \n",
              "25123    hsa-mir-513c             NaN                0                2   \n",
              "25124    hsa-mir-513b             NaN                0                2   \n",
              "25125  hsa-mir-513a-1             NaN                0                2   \n",
              "25126  hsa-mir-513a-2             NaN                0                2   \n",
              "25127     hsa-mir-224             NaN                0                2   \n",
              "\n",
              "       TCGA-2J-AAB6-01  TCGA-2J-AAB8-01  TCGA-2J-AAB9-01  TCGA-2J-AABA-01  \\\n",
              "0                    1                0                0               -1   \n",
              "1                    1                0                0               -1   \n",
              "2                    1                0                0               -1   \n",
              "3                    1                0                0               -1   \n",
              "4                    1                0                0               -1   \n",
              "...                ...              ...              ...              ...   \n",
              "25123                2                0                0                0   \n",
              "25124                2                0                0                0   \n",
              "25125                2                0                0                0   \n",
              "25126                2                0                0                0   \n",
              "25127                2                0                0                0   \n",
              "\n",
              "       TCGA-2J-AABE-01  TCGA-2J-AABF-01  ...  TCGA-XD-AAUG-01  \\\n",
              "0                    0                1  ...                0   \n",
              "1                    0                1  ...                0   \n",
              "2                    0                1  ...                0   \n",
              "3                    0                1  ...                0   \n",
              "4                    0                1  ...                0   \n",
              "...                ...              ...  ...              ...   \n",
              "25123                0                0  ...                0   \n",
              "25124                0                0  ...                0   \n",
              "25125                0                0  ...                0   \n",
              "25126                0                0  ...                0   \n",
              "25127                0                0  ...                0   \n",
              "\n",
              "       TCGA-XD-AAUH-01  TCGA-XD-AAUI-01  TCGA-XD-AAUL-01  TCGA-XN-A8T3-01  \\\n",
              "0                    0                0                0                0   \n",
              "1                    0                0                0                0   \n",
              "2                    0                0                0                0   \n",
              "3                    0                0                0                0   \n",
              "4                    0                0                0                0   \n",
              "...                ...              ...              ...              ...   \n",
              "25123                0                0                0                0   \n",
              "25124                0                0                0                0   \n",
              "25125                0                0                0                0   \n",
              "25126                0                0                0                0   \n",
              "25127                0                0                0                0   \n",
              "\n",
              "       TCGA-XN-A8T5-01  TCGA-YB-A89D-01  TCGA-YH-A8SY-01  TCGA-YY-A8LH-01  \\\n",
              "0                    0                0                0               -1   \n",
              "1                    0                0                0               -1   \n",
              "2                    0                0                0               -1   \n",
              "3                    0                0                0               -1   \n",
              "4                    0                0                0               -1   \n",
              "...                ...              ...              ...              ...   \n",
              "25123                0                0                0                0   \n",
              "25124                0                0                0                0   \n",
              "25125                0                0                0                0   \n",
              "25126                0                0                0                0   \n",
              "25127                0                0                0                0   \n",
              "\n",
              "       TCGA-Z5-AAPL-01  \n",
              "0                    0  \n",
              "1                    0  \n",
              "2                    0  \n",
              "3                    0  \n",
              "4                    0  \n",
              "...                ...  \n",
              "25123                0  \n",
              "25124                0  \n",
              "25125                0  \n",
              "25126                0  \n",
              "25127                0  \n",
              "\n",
              "[25128 rows x 185 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hugo_Symbol</th>\n",
              "      <th>Entrez_Gene_Id</th>\n",
              "      <th>TCGA-2J-AAB1-01</th>\n",
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              "      <th>0</th>\n",
              "      <td>ACAP3</td>\n",
              "      <td>116983.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ACTRT2</td>\n",
              "      <td>140625.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AGRN</td>\n",
              "      <td>375790.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANKRD65</td>\n",
              "      <td>441869.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ATAD3A</td>\n",
              "      <td>55210.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>hsa-mir-513c</td>\n",
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              "    <tr>\n",
              "      <th>25124</th>\n",
              "      <td>hsa-mir-513b</td>\n",
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              "      <td>hsa-mir-513a-1</td>\n",
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              "      <td>hsa-mir-513a-2</td>\n",
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              "    <tr>\n",
              "      <th>25127</th>\n",
              "      <td>hsa-mir-224</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cna_data"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# location of file containing the copy number alterations for each gene and sample\n",
        "file_path = 'data_cna.txt'\n",
        "# import the dataset as a dataframe\n",
        "cna_data = pd.read_table(file_path)\n",
        "cna_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77bce21c-d536-46ee-8002-c65eeea1375d",
      "metadata": {
        "id": "77bce21c-d536-46ee-8002-c65eeea1375d"
      },
      "source": [
        "### 3. Analyze the Mutations of Genes in Wnt Pathway\n",
        "***NOTE:*** A list of genes associated with the WNT signaling pathway was downloaded from the Harmonizome API [[5]](https://maayanlab.cloud/Harmonizome/gene_set/Wnt+signaling+pathway/PANTHER+Pathways). I then performed the following command in bash to extract only the names of the genes.\n",
        "```bash\n",
        "grep -o '\"symbol\":\"[A-Z0-9]*\"' wnt_genes.txt | cut -d '\"' -f 4 > wnt_gene_list.txt\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b995cad0-6357-4bcb-a0ec-efd28bed74c0",
      "metadata": {
        "id": "b995cad0-6357-4bcb-a0ec-efd28bed74c0"
      },
      "source": [
        "#### a. Make a figure (such as from cBioPortal) summarizing the mutations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe807a18-26c0-4f0e-a822-f387552b1847",
      "metadata": {
        "id": "fe807a18-26c0-4f0e-a822-f387552b1847"
      },
      "outputs": [],
      "source": [
        "# initialize an empty list to store the words\n",
        "wnt_genes = []\n",
        "\n",
        "# read in the text file\n",
        "with open('wnt_gene_list.txt', 'r') as file:\n",
        "    # Iterate over each line in the file\n",
        "    for line in file:\n",
        "        # add each gene to the running list of genes\n",
        "        wnt_genes.append(line.strip())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ee4687de-d183-41cf-bf57-c2cc6f4cd102",
      "metadata": {
        "id": "ee4687de-d183-41cf-bf57-c2cc6f4cd102",
        "outputId": "7144eb1f-d8e3-4878-a543-62012ce4044e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-c6f32b76089a>:4: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  mutations = pd.read_table(file_path)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Hugo_Symbol  Entrez_Gene_Id Center NCBI_Build Chromosome  Start_Position  \\\n",
              "19        PCDH9          5101.0      .     GRCh37         13        66878827   \n",
              "66       PCDHA9          9752.0      .     GRCh37          5       140229874   \n",
              "67       PCDHA9          9752.0      .     GRCh37          5       140230003   \n",
              "91        SMAD4          4089.0      .     GRCh37         18        48584504   \n",
              "132       PCDH9          5101.0      .     GRCh37         13        66878849   \n",
              "\n",
              "     End_Position Strand         Consequence Variant_Classification  ...  \\\n",
              "19       66878827      +    missense_variant      Missense_Mutation  ...   \n",
              "66      140229874      +  synonymous_variant                 Silent  ...   \n",
              "67      140230003      +  synonymous_variant                 Silent  ...   \n",
              "91       48584504      +  frameshift_variant        Frame_Shift_Del  ...   \n",
              "132      66878849      +    missense_variant      Missense_Mutation  ...   \n",
              "\n",
              "    SYMBOL_SOURCE                                             TREMBL TSL  \\\n",
              "19           HGNC                                                  .   .   \n",
              "66           HGNC                                                  .   .   \n",
              "67           HGNC                                                  .   .   \n",
              "91           HGNC  Q9BYG6_HUMAN,K7ENG8_HUMAN,K7ENG1_HUMAN,K7EL18_...   .   \n",
              "132          HGNC                                                  .   .   \n",
              "\n",
              "           UNIPARC VARIANT_CLASS  \\\n",
              "19   UPI00001FCE90           SNV   \n",
              "66   UPI00001273D1           SNV   \n",
              "67   UPI00001273D1           SNV   \n",
              "91   UPI0000053431      deletion   \n",
              "132  UPI00001FCE90           SNV   \n",
              "\n",
              "                                           all_effects cDNA_position n_depth  \\\n",
              "19   PCDH9,missense_variant,p.Ala1191Val,ENST000003...          4366      61   \n",
              "66   PCDHA9,synonymous_variant,p.%3D,ENST0000037812...          2827      75   \n",
              "67   PCDHA9,synonymous_variant,p.%3D,ENST0000037812...          2956      97   \n",
              "91   SMAD4,frameshift_variant,p.Ser227ValfsTer14,EN...          1215      42   \n",
              "132  PCDH9,missense_variant,p.Asn1184Tyr,ENST000003...          4344      60   \n",
              "\n",
              "    t_depth Annotation_Status  \n",
              "19      300           SUCCESS  \n",
              "66      417           SUCCESS  \n",
              "67      404           SUCCESS  \n",
              "91      213           SUCCESS  \n",
              "132     335           SUCCESS  \n",
              "\n",
              "[5 rows x 114 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Hugo_Symbol</th>\n",
              "      <th>Entrez_Gene_Id</th>\n",
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              "      <th>Variant_Classification</th>\n",
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              "      <th>VARIANT_CLASS</th>\n",
              "      <th>all_effects</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>PCDH9</td>\n",
              "      <td>5101.0</td>\n",
              "      <td>.</td>\n",
              "      <td>GRCh37</td>\n",
              "      <td>13</td>\n",
              "      <td>66878827</td>\n",
              "      <td>66878827</td>\n",
              "      <td>+</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>Missense_Mutation</td>\n",
              "      <td>...</td>\n",
              "      <td>HGNC</td>\n",
              "      <td>.</td>\n",
              "      <td>.</td>\n",
              "      <td>UPI00001FCE90</td>\n",
              "      <td>SNV</td>\n",
              "      <td>PCDH9,missense_variant,p.Ala1191Val,ENST000003...</td>\n",
              "      <td>4366</td>\n",
              "      <td>61</td>\n",
              "      <td>300</td>\n",
              "      <td>SUCCESS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>PCDHA9</td>\n",
              "      <td>9752.0</td>\n",
              "      <td>.</td>\n",
              "      <td>GRCh37</td>\n",
              "      <td>5</td>\n",
              "      <td>140229874</td>\n",
              "      <td>140229874</td>\n",
              "      <td>+</td>\n",
              "      <td>synonymous_variant</td>\n",
              "      <td>Silent</td>\n",
              "      <td>...</td>\n",
              "      <td>HGNC</td>\n",
              "      <td>.</td>\n",
              "      <td>.</td>\n",
              "      <td>UPI00001273D1</td>\n",
              "      <td>SNV</td>\n",
              "      <td>PCDHA9,synonymous_variant,p.%3D,ENST0000037812...</td>\n",
              "      <td>2827</td>\n",
              "      <td>75</td>\n",
              "      <td>417</td>\n",
              "      <td>SUCCESS</td>\n",
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              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>PCDHA9</td>\n",
              "      <td>9752.0</td>\n",
              "      <td>.</td>\n",
              "      <td>GRCh37</td>\n",
              "      <td>5</td>\n",
              "      <td>140230003</td>\n",
              "      <td>140230003</td>\n",
              "      <td>+</td>\n",
              "      <td>synonymous_variant</td>\n",
              "      <td>Silent</td>\n",
              "      <td>...</td>\n",
              "      <td>HGNC</td>\n",
              "      <td>.</td>\n",
              "      <td>.</td>\n",
              "      <td>UPI00001273D1</td>\n",
              "      <td>SNV</td>\n",
              "      <td>PCDHA9,synonymous_variant,p.%3D,ENST0000037812...</td>\n",
              "      <td>2956</td>\n",
              "      <td>97</td>\n",
              "      <td>404</td>\n",
              "      <td>SUCCESS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>SMAD4</td>\n",
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              "      <td>.</td>\n",
              "      <td>GRCh37</td>\n",
              "      <td>18</td>\n",
              "      <td>48584504</td>\n",
              "      <td>48584504</td>\n",
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              "      <td>frameshift_variant</td>\n",
              "      <td>Frame_Shift_Del</td>\n",
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              "      <td>Q9BYG6_HUMAN,K7ENG8_HUMAN,K7ENG1_HUMAN,K7EL18_...</td>\n",
              "      <td>.</td>\n",
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              "      <td>1215</td>\n",
              "      <td>42</td>\n",
              "      <td>213</td>\n",
              "      <td>SUCCESS</td>\n",
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              "      <td>PCDH9</td>\n",
              "      <td>5101.0</td>\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-51f99d5c-9963-4425-9acb-87504edec45b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-51f99d5c-9963-4425-9acb-87504edec45b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0e98aa67-4785-4380-91a2-0c71731d3597\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e98aa67-4785-4380-91a2-0c71731d3597')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0e98aa67-4785-4380-91a2-0c71731d3597 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wnt_mutations"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# location of file\n",
        "file_path = 'data_mutations.txt'\n",
        "# read in the file as a dataframe\n",
        "mutations = pd.read_table(file_path)\n",
        "# create a dataframe containing only the genes that are in the wnt pathway\n",
        "wnt_mutations = mutations.loc[mutations['Hugo_Symbol'].isin(wnt_genes)]\n",
        "wnt_mutations.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c80fddb4-cb87-4ebe-93f5-0385947468e4",
      "metadata": {
        "id": "c80fddb4-cb87-4ebe-93f5-0385947468e4",
        "outputId": "7e2d2694-ba5d-4484-cf9e-413f621517a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              sample  # mutations\n",
              "91   TCGA-IB-7651-01          502\n",
              "60   TCGA-HV-A5A3-01           13\n",
              "65   TCGA-HV-A7OP-01            8\n",
              "88   TCGA-IB-7644-01            6\n",
              "29   TCGA-3A-A9J0-01            6\n",
              "..               ...          ...\n",
              "37   TCGA-FB-A5VM-01            1\n",
              "36   TCGA-FB-A545-01            1\n",
              "108  TCGA-IB-AAUM-01            1\n",
              "112  TCGA-IB-AAUR-01            1\n",
              "137  TCGA-Z5-AAPL-01            1\n",
              "\n",
              "[138 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1323af48-e4b8-4fed-b603-15046b39df5c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th># mutations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>TCGA-IB-7651-01</td>\n",
              "      <td>502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>TCGA-HV-A5A3-01</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>TCGA-HV-A7OP-01</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>TCGA-IB-7644-01</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>TCGA-3A-A9J0-01</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>TCGA-FB-A5VM-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>TCGA-FB-A545-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>TCGA-IB-AAUM-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>TCGA-IB-AAUR-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>TCGA-Z5-AAPL-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1323af48-e4b8-4fed-b603-15046b39df5c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1323af48-e4b8-4fed-b603-15046b39df5c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1323af48-e4b8-4fed-b603-15046b39df5c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5615aaa7-345b-4b14-bbb3-77cf5b4e30df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5615aaa7-345b-4b14-bbb3-77cf5b4e30df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5615aaa7-345b-4b14-bbb3-77cf5b4e30df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d6ada041-8f7d-4c5f-be73-b26a942792fa\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "num_mutations",
              "summary": "{\n  \"name\": \"num_mutations\",\n  \"rows\": 138,\n  \"fields\": [\n    {\n      \"column\": \"sample\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 138,\n        \"samples\": [\n          \"TCGA-HV-A7OL-01\",\n          \"TCGA-2J-AABH-01\",\n          \"TCGA-IB-7647-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"# mutations\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 1,\n        \"max\": 502,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2,\n          13,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# create a dataframe containing the number of mutations each tumor has\n",
        "num_mutations = wnt_mutations.groupby('Tumor_Sample_Barcode').size().reset_index()\n",
        "# rename the columns\n",
        "num_mutations.columns = ['sample', '# mutations']\n",
        "# sort samples from greatest to fewest number of mutations\n",
        "num_mutations.sort_values('# mutations', ascending=False, inplace=True)\n",
        "num_mutations"
      ]
    },
    {
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      "execution_count": null,
      "id": "e0ab94e2-6105-425d-863e-c4f6aa688e8c",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "outputs": [
        {
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              "                            \n",
              "var gd = document.getElementById('12da0e23-4a61-4e35-b256-93f636c768d4');\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# create a histogram showing the frequency of total number of mutations in samples\n",
        "fig = px.histogram(num_mutations, x='# mutations', nbins=70)\n",
        "\n",
        "# update labels on figure\n",
        "fig.update_layout(\n",
        "    title = 'Figure 1. Frequency of Mutations',\n",
        "    title_x = 0.5,\n",
        "    xaxis_title = 'Number of Mutations',\n",
        "    yaxis_title = 'Number of samples',\n",
        "    bargap=0.2)\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67306dac-2434-4e8d-bafe-5131eb91707c",
      "metadata": {
        "id": "67306dac-2434-4e8d-bafe-5131eb91707c"
      },
      "source": [
        "#### b. How many patients have each gene mutated with either copy number alterations or point mutations?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c39ce154-cf09-4a39-bd73-0ad92c491b3c",
      "metadata": {
        "id": "c39ce154-cf09-4a39-bd73-0ad92c491b3c",
        "outputId": "31fc5768-e0d4-40d3-8da4-21a71fbf49f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hugo_Symbol  TCGA-2J-AAB1-01  TCGA-2J-AAB4-01  TCGA-2J-AAB6-01  \\\n",
              "18           DVL1                0                0                1   \n",
              "26           GNB1                0                0                1   \n",
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              "442        ARID1A                1                0                1   \n",
              "...           ...              ...              ...              ...   \n",
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              "\n",
              "       TCGA-2J-AABF-01  TCGA-2J-AABH-01  ...  TCGA-XD-AAUG-01  \\\n",
              "18                   1                1  ...                0   \n",
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              "24389                0                0  ...                0   \n",
              "24463                0                0  ...                0   \n",
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              "       TCGA-XD-AAUH-01  TCGA-XD-AAUI-01  TCGA-XD-AAUL-01  TCGA-XN-A8T3-01  \\\n",
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              "18                   0                0                0                1   \n",
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              "<p>252 rows × 184 columns</p>\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "variable_name": "has_cnas"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# create a dataframe that contains only the copy number alterations in genes in the Wnt pathway\n",
        "wnt_cnas = cna_data.loc[cna_data['Hugo_Symbol'].isin(wnt_genes)]\n",
        "# create a dataframe containing whether or not the sample has a copy number alteration at gene\n",
        "has_cnas = wnt_cnas.drop(columns=['Hugo_Symbol', 'Entrez_Gene_Id'], axis=1).abs()\n",
        "# add back the column with the names of the genes\n",
        "has_cnas = pd.concat([wnt_cnas['Hugo_Symbol'], has_cnas], axis=1)\n",
        "has_cnas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47e9d289-5b78-4205-9a8b-76d375e96fe8",
      "metadata": {
        "id": "47e9d289-5b78-4205-9a8b-76d375e96fe8"
      },
      "outputs": [],
      "source": [
        "### function to determine if each tumor sample has a point mutation in one gene\n",
        "def samples_with_mut(muts_in_gene, wnt_mut):\n",
        "    ## store the name of the gene\n",
        "    gene = muts_in_gene['Hugo_Symbol']\n",
        "    ## store the name of all the samples with a point mutation in gene\n",
        "    samples_with_mut = wnt_mut.loc[wnt_mut['Hugo_Symbol'] == gene, 'Tumor_Sample_Barcode'].unique()\n",
        "    ## for each sample with a point mutation,\n",
        "    for sample in samples_with_mut:\n",
        "        # record that the sample has a mutation in this gene\n",
        "        muts_in_gene[sample] = 1\n",
        "    ## fill the remaining samples with 0 (no mutation in gene)\n",
        "    muts_in_gene.fillna(0, inplace=True)\n",
        "    return muts_in_gene"
      ]
    },
    {
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      "execution_count": null,
      "id": "133c49a6-83a9-4178-8fa1-042df6484735",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Hugo_Symbol  TCGA-2J-AAB1-01  TCGA-2J-AAB4-01  TCGA-2J-AAB6-01  \\\n",
              "0         PCDH9                1                0                1   \n",
              "1        PCDHA9                1                0                0   \n",
              "2         SRCAP                0                0                0   \n",
              "3         SMAD4                0                0                0   \n",
              "4        PCDHA3                0                0                0   \n",
              "..          ...              ...              ...              ...   \n",
              "207        GNB2                0                0                0   \n",
              "208       HDAC3                0                0                0   \n",
              "209        GNG4                0                0                0   \n",
              "210      CSNK2B                0                0                0   \n",
              "211       GNA14                0                0                0   \n",
              "\n",
              "     TCGA-2J-AAB8-01  TCGA-2J-AAB9-01  TCGA-2J-AABA-01  TCGA-2J-AABE-01  \\\n",
              "0                  0                0                0                0   \n",
              "1                  0                0                0                0   \n",
              "2                  1                0                0                0   \n",
              "3                  1                0                1                0   \n",
              "4                  0                1                0                0   \n",
              "..               ...              ...              ...              ...   \n",
              "207                0                0                0                0   \n",
              "208                0                0                0                0   \n",
              "209                0                0                0                0   \n",
              "210                0                0                0                0   \n",
              "211                0                0                0                0   \n",
              "\n",
              "     TCGA-2J-AABF-01  TCGA-2J-AABH-01  ...  TCGA-XD-AAUG-01  TCGA-XD-AAUH-01  \\\n",
              "0                  0                0  ...                0                0   \n",
              "1                  0                0  ...                0                0   \n",
              "2                  0                0  ...                0                0   \n",
              "3                  0                0  ...                0                0   \n",
              "4                  0                0  ...                0                0   \n",
              "..               ...              ...  ...              ...              ...   \n",
              "207                0                0  ...                0                0   \n",
              "208                0                0  ...                0                0   \n",
              "209                0                0  ...                0                0   \n",
              "210                0                0  ...                0                0   \n",
              "211                0                0  ...                0                0   \n",
              "\n",
              "     TCGA-XD-AAUI-01  TCGA-XD-AAUL-01  TCGA-XN-A8T3-01  TCGA-XN-A8T5-01  \\\n",
              "0                  0                0                0                0   \n",
              "1                  0                0                0                0   \n",
              "2                  0                0                0                0   \n",
              "3                  0                0                0                0   \n",
              "4                  0                0                0                0   \n",
              "..               ...              ...              ...              ...   \n",
              "207                0                0                0                0   \n",
              "208                0                0                0                0   \n",
              "209                0                0                0                0   \n",
              "210                0                0                0                0   \n",
              "211                0                0                0                0   \n",
              "\n",
              "     TCGA-YB-A89D-01  TCGA-YH-A8SY-01  TCGA-YY-A8LH-01  TCGA-Z5-AAPL-01  \n",
              "0                  0                0                0                0  \n",
              "1                  1                0                1                0  \n",
              "2                  0                0                0                0  \n",
              "3                  1                0                0                0  \n",
              "4                  0                0                0                0  \n",
              "..               ...              ...              ...              ...  \n",
              "207                0                0                1                0  \n",
              "208                0                0                0                0  \n",
              "209                0                0                0                0  \n",
              "210                0                0                0                0  \n",
              "211                0                0                0                0  \n",
              "\n",
              "[212 rows x 184 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hugo_Symbol</th>\n",
              "      <th>TCGA-2J-AAB1-01</th>\n",
              "      <th>TCGA-2J-AAB4-01</th>\n",
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              "      <th>TCGA-YB-A89D-01</th>\n",
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              "      <th>1</th>\n",
              "      <td>PCDHA9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SRCAP</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>PCDHA3</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-78759705-48d7-40c7-9206-d1ca2176909a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-78759705-48d7-40c7-9206-d1ca2176909a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "    (() => {\n",
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              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('has_mut')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('has_mut');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "has_mut"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# create a dataframe to store whether each tumor sample has a mutation in that gene\n",
        "has_mut = pd.DataFrame(columns=has_cnas.columns)\n",
        "\n",
        "# create a dataframe containing only data about point mutations\n",
        "wnt_point_muts = wnt_mutations[wnt_mutations['Variant_Type'] == 'SNP']\n",
        "# add the list of genes that have a point mutation\n",
        "has_mut['Hugo_Symbol'] = wnt_point_muts['Hugo_Symbol'].unique()\n",
        "\n",
        "# for each gene, add whether each tumor sample has a point mutation at that gene\n",
        "has_mut = has_mut.apply(lambda row: samples_with_mut(row, wnt_point_muts), axis=1)\n",
        "has_mut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "348aa36f-fa67-4d4d-bb39-1f0d596eb47e",
      "metadata": {
        "id": "348aa36f-fa67-4d4d-bb39-1f0d596eb47e"
      },
      "outputs": [],
      "source": [
        "### function to determine if each tumor sample has a point mutation or copy number alteration in gene\n",
        "def mut_or_cna(gene, has_mut, has_cnas):\n",
        "    ## if there was 1+ samples that had a point mutation and copy number alteration in this gene\n",
        "    if (gene in has_mut['Hugo_Symbol'].values) and (gene in has_cnas['Hugo_Symbol'].values):\n",
        "        # store whether each sample has a point mutation in gene\n",
        "        gene_has_mut = has_mut.loc[has_mut['Hugo_Symbol'] == gene]\n",
        "        # store whether each sample has a copy number alteration in gene\n",
        "        gene_has_cna = has_cnas.loc[has_cnas['Hugo_Symbol'] == gene]\n",
        "        # combine the two into one dataframe\n",
        "        mut_cna = pd.concat([gene_has_mut, gene_has_cna])\n",
        "        # store whether each sample had a point mutation or copy number alteration\n",
        "        mut_or_cna = (mut_cna.drop('Hugo_Symbol', axis=1).sum() > 0).astype(int)\n",
        "        # return this information\n",
        "        return mut_or_cna\n",
        "    ## otherwise, samples only had a copy number alteration in this gene\n",
        "    elif gene in has_cnas['Hugo_Symbol'].values:\n",
        "        # store whether each sample has a copy number alteration in gene\n",
        "        gene_has_cna = has_cnas.loc[has_cnas['Hugo_Symbol'] == gene].drop('Hugo_Symbol', axis=1)\n",
        "        # return this information\n",
        "        return gene_has_cna.iloc[0]\n",
        "    ## otherwise, samples only had point mutations in gene\n",
        "    else:\n",
        "        # store whether each sample has a point mutation in gene\n",
        "        gene_has_mut = has_mut.loc[has_mut['Hugo_Symbol'] == gene].drop('Hugo_Symbol', axis=1)\n",
        "        # return this information\n",
        "        return gene_has_mut.iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "376c706a-486d-49ae-850c-b6c151294fbc",
      "metadata": {
        "id": "376c706a-486d-49ae-850c-b6c151294fbc",
        "outputId": "95d25c04-84a4-45e3-a053-d5c66f43c8b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            TCGA-2J-AAB1-01 TCGA-2J-AAB4-01 TCGA-2J-AAB6-01 TCGA-2J-AAB8-01  \\\n",
              "Hugo_Symbol                                                                   \n",
              "SMAD4                     0               1               1               1   \n",
              "CDH20                     0               1               1               1   \n",
              "CDH19                     0               1               1               1   \n",
              "CDH7                      0               1               1               1   \n",
              "NFATC1                    0               1               1               1   \n",
              "...                     ...             ...             ...             ...   \n",
              "CTNNA2                    0               0               0               0   \n",
              "FRZB                      0               0               0               0   \n",
              "TCF7L1                    0               0               0               0   \n",
              "PPP3R1                    0               0               0               0   \n",
              "PCDH11Y                   0               0               0               0   \n",
              "\n",
              "            TCGA-2J-AAB9-01 TCGA-2J-AABA-01 TCGA-2J-AABE-01 TCGA-2J-AABF-01  \\\n",
              "Hugo_Symbol                                                                   \n",
              "SMAD4                     0               1               1               1   \n",
              "CDH20                     0               1               1               1   \n",
              "CDH19                     0               1               1               1   \n",
              "CDH7                      0               1               1               1   \n",
              "NFATC1                    0               1               1               1   \n",
              "...                     ...             ...             ...             ...   \n",
              "CTNNA2                    0               0               0               1   \n",
              "FRZB                      0               0               1               0   \n",
              "TCF7L1                    0               1               0               0   \n",
              "PPP3R1                    0               0               0               0   \n",
              "PCDH11Y                   0               0               0               0   \n",
              "\n",
              "            TCGA-2J-AABH-01 TCGA-2J-AABI-01  ... TCGA-XD-AAUH-01  \\\n",
              "Hugo_Symbol                                  ...                   \n",
              "SMAD4                     1               1  ...               0   \n",
              "CDH20                     1               1  ...               0   \n",
              "CDH19                     1               1  ...               0   \n",
              "CDH7                      1               1  ...               0   \n",
              "NFATC1                    1               1  ...               0   \n",
              "...                     ...             ...  ...             ...   \n",
              "CTNNA2                    0               0  ...               0   \n",
              "FRZB                      0               0  ...               0   \n",
              "TCF7L1                    0               0  ...               0   \n",
              "PPP3R1                    0               0  ...               0   \n",
              "PCDH11Y                   0               0  ...               0   \n",
              "\n",
              "            TCGA-XD-AAUI-01 TCGA-XD-AAUL-01 TCGA-XN-A8T3-01 TCGA-XN-A8T5-01  \\\n",
              "Hugo_Symbol                                                                   \n",
              "SMAD4                     1               1               1               0   \n",
              "CDH20                     1               1               1               0   \n",
              "CDH19                     1               1               1               0   \n",
              "CDH7                      1               1               1               0   \n",
              "NFATC1                    1               1               1               0   \n",
              "...                     ...             ...             ...             ...   \n",
              "CTNNA2                    0               0               0               0   \n",
              "FRZB                      0               0               0               0   \n",
              "TCF7L1                    0               0               0               0   \n",
              "PPP3R1                    0               0               0               0   \n",
              "PCDH11Y                   0               0               0               0   \n",
              "\n",
              "            TCGA-YB-A89D-01 TCGA-YH-A8SY-01 TCGA-YY-A8LH-01 TCGA-Z5-AAPL-01  \\\n",
              "Hugo_Symbol                                                                   \n",
              "SMAD4                     1               1               1               0   \n",
              "CDH20                     1               1               1               0   \n",
              "CDH19                     1               1               1               0   \n",
              "CDH7                      1               1               1               0   \n",
              "NFATC1                    1               1               1               0   \n",
              "...                     ...             ...             ...             ...   \n",
              "CTNNA2                    1               1               0               0   \n",
              "FRZB                      1               1               0               0   \n",
              "TCF7L1                    1               1               0               0   \n",
              "PPP3R1                    1               1               0               0   \n",
              "PCDH11Y                   0               0               0               0   \n",
              "\n",
              "            total # samples  \n",
              "Hugo_Symbol                  \n",
              "SMAD4                   136  \n",
              "CDH20                   131  \n",
              "CDH19                   131  \n",
              "CDH7                    131  \n",
              "NFATC1                  128  \n",
              "...                     ...  \n",
              "CTNNA2                   27  \n",
              "FRZB                     27  \n",
              "TCF7L1                   26  \n",
              "PPP3R1                   25  \n",
              "PCDH11Y                   1  \n",
              "\n",
              "[253 rows x 184 columns]"
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SMAD4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CDH20</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CDH19</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CDH7</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NFATC1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CTNNA2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FRZB</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TCF7L1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PPP3R1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PCDH11Y</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>253 rows × 184 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c258d31c-8daa-46e9-9e86-43aa6250dc65')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c258d31c-8daa-46e9-9e86-43aa6250dc65 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c258d31c-8daa-46e9-9e86-43aa6250dc65');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9df9ef48-3a98-44b5-84de-a19aa0e99dfb\">\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9df9ef48-3a98-44b5-84de-a19aa0e99dfb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2e03f490-9b4d-4bf5-8b69-d8e428af9184\">\n",
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              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('has_mut_or_cna')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2e03f490-9b4d-4bf5-8b69-d8e428af9184 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('has_mut_or_cna');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "variable_name": "has_mut_or_cna"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "### create a dataframe that contains whether each patient has a copy number alteration or mutation in each gene\n",
        "has_mut_or_cna = pd.DataFrame(columns=has_mut.columns)\n",
        "### add a column containing all the genes that have either a point mutation or copy number alteration\n",
        "genes = list(set(has_mut['Hugo_Symbol']) | set(has_cnas['Hugo_Symbol']))\n",
        "has_mut_or_cna['Hugo_Symbol'] = genes\n",
        "### set index as the genes\n",
        "has_mut_or_cna.set_index('Hugo_Symbol', inplace=True)\n",
        "### iterate through each gene\n",
        "for gene in genes:\n",
        "    ## add whether each sample had a point mutation or copy number alteration in each gene\n",
        "    gene_data = mut_or_cna(gene, has_mut, has_cnas)\n",
        "    ## add this information to the dataframe\n",
        "    has_mut_or_cna.loc[gene] = gene_data\n",
        "\n",
        "### add a column with the total number of samples with any point mutations or copy alterations in gene\n",
        "has_mut_or_cna['total # samples'] = has_mut_or_cna.sum(axis=1)\n",
        "### sort genes from greatest to least amount of mutations in each gene\n",
        "has_mut_or_cna.sort_values('total # samples', ascending=False, inplace=True)\n",
        "has_mut_or_cna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e9b9bde4-6416-4dd0-997d-b2b8d5f52ead",
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        "id": "e9b9bde4-6416-4dd0-997d-b2b8d5f52ead",
        "outputId": "ec125b71-bb14-4246-b53f-6830637df1aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        gene total # samples % of all samples\n",
              "0      SMAD4             136        73.913043\n",
              "1      CDH20             131        71.195652\n",
              "2      CDH19             131        71.195652\n",
              "3       CDH7             131        71.195652\n",
              "4     NFATC1             128        69.565217\n",
              "..       ...             ...              ...\n",
              "248   CTNNA2              27        14.673913\n",
              "249     FRZB              27        14.673913\n",
              "250   TCF7L1              26        14.130435\n",
              "251   PPP3R1              25        13.586957\n",
              "252  PCDH11Y               1         0.543478\n",
              "\n",
              "[253 rows x 3 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>gene</th>\n",
              "      <th>total # samples</th>\n",
              "      <th>% of all samples</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SMAD4</td>\n",
              "      <td>136</td>\n",
              "      <td>73.913043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CDH20</td>\n",
              "      <td>131</td>\n",
              "      <td>71.195652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CDH19</td>\n",
              "      <td>131</td>\n",
              "      <td>71.195652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CDH7</td>\n",
              "      <td>131</td>\n",
              "      <td>71.195652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NFATC1</td>\n",
              "      <td>128</td>\n",
              "      <td>69.565217</td>\n",
              "    </tr>\n",
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              "      <th>248</th>\n",
              "      <td>CTNNA2</td>\n",
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              "      <td>14.673913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>FRZB</td>\n",
              "      <td>27</td>\n",
              "      <td>14.673913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>TCF7L1</td>\n",
              "      <td>26</td>\n",
              "      <td>14.130435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>PPP3R1</td>\n",
              "      <td>25</td>\n",
              "      <td>13.586957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>PCDH11Y</td>\n",
              "      <td>1</td>\n",
              "      <td>0.543478</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>253 rows × 3 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "num_samples_with_mut",
              "summary": "{\n  \"name\": \"num_samples_with_mut\",\n  \"rows\": 253,\n  \"fields\": [\n    {\n      \"column\": \"gene\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 253,\n        \"samples\": [\n          \"FAT3\",\n          \"DVL2\",\n          \"CDH22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total # samples\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1,\n        \"max\": 136,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          136,\n          97,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% of all samples\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.5434782608695652,\n        \"max\": 73.91304347826086,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          73.91304347826086,\n          52.71739130434783,\n          21.195652173913043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# show the total number of samples with a point mutations or copy alteration for each gene\n",
        "num_samples_with_mut = has_mut_or_cna['total # samples'].reset_index()\n",
        "# rename columns\n",
        "num_samples_with_mut.columns = ['gene', 'total # samples']\n",
        "# add a column with the % of all patients in dataset\n",
        "num_samples_with_mut['% of all samples'] = num_samples_with_mut['total # samples']/float(184)*100\n",
        "num_samples_with_mut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8b03d009-1d5d-48ea-9bb6-64b022e1fc22",
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        "outputId": "5680181f-e038-45ac-93ab-c4c0f35514e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
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          "data": {
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          "metadata": {}
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      ],
      "source": [
        "# create a bar chart showing the number of patients with mutations in gene\n",
        "fig = px.bar(num_samples_with_mut, x='gene', y='total # samples')\n",
        "\n",
        "# update labels on figure\n",
        "fig.update_layout(\n",
        "    title = 'Figure 2. Number of Patients with a Copy Number Alteration or Point Mutation in Gene',\n",
        "    title_x = 0.5,\n",
        "    xaxis_title = 'Gene',\n",
        "    yaxis_title = 'Number of Patients',\n",
        "    width = 1050\n",
        ")\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2528020-0d5c-4404-ac05-c7f31f6e4ced",
      "metadata": {
        "id": "f2528020-0d5c-4404-ac05-c7f31f6e4ced"
      },
      "source": [
        "#### c. For the top 3 most mutated genes in the Wnt pathway:\n",
        "1. How often is each gene mutated?\n",
        "2. Are the mutations are activating or inactivating? Show evidence.\n",
        "3. Is the mutation pattern consistent with oncogene or tumor suppressor activity in cancer?"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    gene total # samples % of all samples\n",
              "0  SMAD4             136        73.913043\n",
              "1  CDH20             131        71.195652\n",
              "2  CDH19             131        71.195652\n",
              "3   CDH7             131        71.195652"
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              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "num_samples_with_mut",
              "summary": "{\n  \"name\": \"num_samples_with_mut\",\n  \"rows\": 253,\n  \"fields\": [\n    {\n      \"column\": \"gene\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 253,\n        \"samples\": [\n          \"FAT3\",\n          \"DVL2\",\n          \"CDH22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total # samples\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1,\n        \"max\": 136,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          136,\n          97,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% of all samples\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.5434782608695652,\n        \"max\": 73.91304347826086,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          73.91304347826086,\n          52.71739130434783,\n          21.195652173913043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# show the top 4 most mutated genes\n",
        "num_samples_with_mut.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cea8bb2b-e562-4c49-bc5b-90523ee40b2a",
      "metadata": {
        "id": "cea8bb2b-e562-4c49-bc5b-90523ee40b2a"
      },
      "outputs": [],
      "source": [
        "### function to analyze types of mutations for one gene\n",
        "def analyze_mut(gene, wnt_mut, wnt_cna):\n",
        "    ## create a dataframe containing all of the mutations in gene\n",
        "    gene_mut = wnt_mutations[wnt_mutations['Hugo_Symbol'] == gene]\n",
        "    ## select the CNA levels for gene of interest\n",
        "    gene_cna = wnt_cna[wnt_cna['Hugo_Symbol'] == gene]\n",
        "    ## have the cna as a column\n",
        "    gene_cna = gene_cna.iloc[0, 2:].reset_index()\n",
        "    ## rename columns\n",
        "    gene_cna.columns = ['sample', 'CNA']\n",
        "    ## remove numbers at end of patient name\n",
        "    gene_cna['sample'] = gene_cna['sample'].str[:-3]\n",
        "\n",
        "    ## create a dataframe showing the number of mutations of each type\n",
        "    gene_mut_stats = gene_mut['Variant_Classification'].value_counts().reset_index()\n",
        "    ## rename the column\n",
        "    gene_mut_stats.columns = ['mutation type', '# samples']\n",
        "    ## add a column with % of all mutations\n",
        "    total_mut = gene_mut_stats['# samples'].sum()\n",
        "    gene_mut_stats['%'] = gene_mut_stats['# samples']/float(total_mut) * 100\n",
        "    print(f'Summary of Mutations in {gene}')\n",
        "    print(gene_mut_stats)\n",
        "\n",
        "    ## create a dataframe with the summary statistics for the copy number alterations\n",
        "    gene_cna_stats = gene_cna['CNA'].value_counts().reset_index()\n",
        "    ## rename the columns\n",
        "    gene_cna_stats.columns = ['CNA type', '# samples']\n",
        "    ## add a column with % of all copy number alterations\n",
        "    total_cna = gene_cna_stats['# samples'].sum()\n",
        "    gene_cna_stats['%'] = gene_cna_stats['# samples']/float(total_cna) * 100\n",
        "    print(f'\\nSummary of Copy Number Alterations in {gene}')\n",
        "    print(gene_cna_stats)\n",
        "\n",
        "    return gene_mut, gene_cna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aba108b7-4c97-48b7-92a7-ba668cdf1a25",
      "metadata": {
        "id": "aba108b7-4c97-48b7-92a7-ba668cdf1a25",
        "outputId": "a132a434-41de-44cf-cea7-1f1ff7705b24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of Mutations in SMAD4\n",
            "       mutation type  # samples     %\n",
            "0  Missense_Mutation         16  40.0\n",
            "1  Nonsense_Mutation         12  30.0\n",
            "2    Frame_Shift_Del          7  17.5\n",
            "3    Frame_Shift_Ins          3   7.5\n",
            "4        Splice_Site          1   2.5\n",
            "5       In_Frame_Del          1   2.5\n",
            "\n",
            "Summary of Copy Number Alterations in SMAD4\n",
            "   CNA type  # samples          %\n",
            "0        -1        102  55.737705\n",
            "1         0         51  27.868852\n",
            "2        -2         23  12.568306\n",
            "3         1          7   3.825137\n"
          ]
        }
      ],
      "source": [
        "# get summary of mutations and copy number alterations for the most mutated gene\n",
        "SMAD4_mut, SMAD4_cna = analyze_mut('SMAD4', wnt_mutations, wnt_cnas)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9974f0a-a7e8-482e-a3da-b4b2151f5691",
      "metadata": {
        "id": "b9974f0a-a7e8-482e-a3da-b4b2151f5691"
      },
      "source": [
        "**SMAD4**\n",
        "> 1. About 74% of all patients had mutations in this gene (copy number alteration or point mutation).\n",
        ">\n",
        "> 2. Mutations in this gene seem to be inactivating as there is a high percentage (50%) of mutations that result in a loss of function in the resulting protein. Additionally, it seems that 56% of all patients have a hemizygous deletion for this gene.\n",
        ">\n",
        "> 3. Tumor suppressor genes typically act to prevent cancer formation by inhibiting cell growth or promoting apoptosis. This means that inactivating or having deletions in tumor suppressor genes, would promote cell growth. These results are consistent with this gene, suggesting that it is a tumor suppressor."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32940dd3-d493-49d5-a043-93c3e33ae673",
      "metadata": {
        "id": "32940dd3-d493-49d5-a043-93c3e33ae673"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b507f33-eb9c-4154-9082-ee8b2f03d466",
      "metadata": {
        "id": "2b507f33-eb9c-4154-9082-ee8b2f03d466",
        "outputId": "d0a5a212-509b-4477-8290-662c49244069",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of Mutations in CDH7\n",
            "       mutation type  # samples      %\n",
            "0  Missense_Mutation          1  100.0\n",
            "\n",
            "Summary of Copy Number Alterations in CDH7\n",
            "   CNA type  # samples          %\n",
            "0        -1        117  63.934426\n",
            "1         0         53  28.961749\n",
            "2        -2          6   3.278689\n",
            "3         1          6   3.278689\n",
            "4         2          1   0.546448\n"
          ]
        }
      ],
      "source": [
        "# get summary of mutations and copy number alterations for the 2nd most mutated gene\n",
        "CDH7_mut, CDH7_cna = analyze_mut('CDH7', wnt_mutations, wnt_cnas)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8beb0d4c-a412-4470-89cf-1491421f4fc8",
      "metadata": {
        "id": "8beb0d4c-a412-4470-89cf-1491421f4fc8"
      },
      "source": [
        "**CDH7**\n",
        "> 1. About 71% of all patients had mutations in this gene (copy number alteration or point mutation).\n",
        ">\n",
        "> 2. The patients predominantly (64% of all patients) have a hemizygous deletion for this gene.\n",
        ">\n",
        "> 3. The significant proportion of deletions suggests a loss of gene function, which is more consistent with tumor suppressor activity."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03deffa5-d569-4bcf-bd1f-641d485cfe14",
      "metadata": {
        "id": "03deffa5-d569-4bcf-bd1f-641d485cfe14"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b948d20-d598-48a1-b751-697ddd793c74",
      "metadata": {
        "id": "3b948d20-d598-48a1-b751-697ddd793c74",
        "outputId": "bbc036ac-feb7-4c78-a8b0-5e5e047e2e16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of Mutations in CDH20\n",
            "       mutation type  # samples          %\n",
            "0  Missense_Mutation          2  66.666667\n",
            "1             Silent          1  33.333333\n",
            "\n",
            "Summary of Copy Number Alterations in CDH20\n",
            "   CNA type  # samples          %\n",
            "0        -1        118  64.480874\n",
            "1         0         53  28.961749\n",
            "2         1          6   3.278689\n",
            "3        -2          5   2.732240\n",
            "4         2          1   0.546448\n"
          ]
        }
      ],
      "source": [
        "# get summary of mutations and copy number alterations for the 3rd most mutated gene\n",
        "CDH20_mut, CDH20_cna = analyze_mut('CDH20', wnt_mutations, wnt_cnas)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e0ed91fd-3c4c-484d-a17c-d79beba38aed",
      "metadata": {
        "id": "e0ed91fd-3c4c-484d-a17c-d79beba38aed"
      },
      "source": [
        "**CDH20**\n",
        "> 1. About 71.2% of all patients had mutations in this gene (copy number alteration or point mutation).\n",
        ">\n",
        "> 2. The patients predominantly (64% of all patients) have a hemizygous deletion for this gene.\n",
        ">\n",
        "> 3. The significant proportion of deletions suggests a loss of gene function, which is more consistent with tumor suppressor activity."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d41dcc95-ae87-4466-8578-cf7f17245fab",
      "metadata": {
        "id": "d41dcc95-ae87-4466-8578-cf7f17245fab"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c20a1c01-dbce-4831-bf52-f1302035aab1",
      "metadata": {
        "id": "c20a1c01-dbce-4831-bf52-f1302035aab1"
      },
      "source": [
        "#### d. Which of your top 3 mutations is associated with increased or decreased survival outcome?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### function to relabel the survival outcome for each patient\n",
        "def classify_status(dss_status):\n",
        "  ## store the numeric value for DSS status\n",
        "  int_status = int(dss_status[0])\n",
        "  ## if it is 0,\n",
        "  if int_status == 1:\n",
        "    # label patient as tumor free\n",
        "    new_label = 'Tumor Free'\n",
        "  ## otherwise,\n",
        "  else:\n",
        "    # label patient as with tumor\n",
        "    new_label = 'With Tumor'\n",
        "  return new_label"
      ],
      "metadata": {
        "id": "izCzruvt_Pt_"
      },
      "id": "izCzruvt_Pt_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bae92ea7-6df4-444a-84d7-789144f664fb",
      "metadata": {
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        "outputId": "f9de7063-16e2-4c29-e3e2-47399fc901d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PATIENT_ID  DSS_STATUS\n",
              "0    TCGA-2J-AAB1  Tumor Free\n",
              "1    TCGA-2J-AAB4  With Tumor\n",
              "2    TCGA-2J-AAB6  Tumor Free\n",
              "3    TCGA-2J-AAB8  With Tumor\n",
              "4    TCGA-2J-AAB9  Tumor Free\n",
              "..            ...         ...\n",
              "179  TCGA-XN-A8T5  With Tumor\n",
              "180  TCGA-YB-A89D  With Tumor\n",
              "181  TCGA-YH-A8SY  With Tumor\n",
              "182  TCGA-YY-A8LH  With Tumor\n",
              "183  TCGA-Z5-AAPL  With Tumor\n",
              "\n",
              "[177 rows x 2 columns]"
            ],
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              "<style scoped>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGA-2J-AAB4</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGA-2J-AAB6</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGA-2J-AAB8</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGA-2J-AAB9</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>TCGA-XN-A8T5</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>TCGA-YB-A89D</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>TCGA-YH-A8SY</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>TCGA-YY-A8LH</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>TCGA-Z5-AAPL</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>177 rows × 2 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "survival_outcome",
              "summary": "{\n  \"name\": \"survival_outcome\",\n  \"rows\": 177,\n  \"fields\": [\n    {\n      \"column\": \"PATIENT_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 177,\n        \"samples\": [\n          \"TCGA-2L-AAQI\",\n          \"TCGA-F2-A44G\",\n          \"TCGA-IB-AAUO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DSS_STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"With Tumor\",\n          \"Tumor Free\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# location of file containing the survival outcomes of patients\n",
        "file_path = 'data_clinical_patient.txt'\n",
        "# import data as a dataframe\n",
        "patient_info = pd.read_table(file_path, skiprows=4)\n",
        "# store a dataframe containing only survival outcome\n",
        "survival_outcome = patient_info[['PATIENT_ID', 'DSS_STATUS']]\n",
        "# remove samples no status recorded\n",
        "survival_outcome = survival_outcome[survival_outcome['DSS_STATUS'].notna()]\n",
        "# relabel the survival outcome\n",
        "survival_outcome['DSS_STATUS'] = survival_outcome['DSS_STATUS'].apply(classify_status)\n",
        "# store a list of all patients for future reference\n",
        "all_patients = survival_outcome['PATIENT_ID']\n",
        "survival_outcome"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# store a list containing the top 3 mutated genes in the Wnt pathway\n",
        "top_genes = ['SMAD4', 'CDH7', 'CDH20']\n",
        "# store the number of mutations in for each patient for each of the top 3 mutated genes\n",
        "total_mut_per_patient = has_mut_or_cna.loc[top_genes].drop('total # samples', axis=1).transpose().reset_index()\n",
        "# rename columns\n",
        "total_mut_per_patient.columns = ['PATIENT_ID'] + top_genes\n",
        "# remove the number barcodes to get just the patient id\n",
        "total_mut_per_patient['PATIENT_ID'] = total_mut_per_patient['PATIENT_ID'].str[:-3]\n",
        "# add survival outcome information to these patients\n",
        "total_mut_per_patient = pd.merge(total_mut_per_patient, survival_outcome, on='PATIENT_ID', how='inner')\n",
        "total_mut_per_patient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BfLkfxRihqyc",
        "outputId": "d3d954d4-a9e4-49a8-ad57-27a773936d84"
      },
      "id": "BfLkfxRihqyc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PATIENT_ID SMAD4 CDH7 CDH20  DSS_STATUS\n",
              "0    TCGA-2J-AAB1     0    0     0  Tumor Free\n",
              "1    TCGA-2J-AAB4     1    1     1  With Tumor\n",
              "2    TCGA-2J-AAB6     1    1     1  Tumor Free\n",
              "3    TCGA-2J-AAB8     1    1     1  With Tumor\n",
              "4    TCGA-2J-AAB9     0    0     0  Tumor Free\n",
              "..            ...   ...  ...   ...         ...\n",
              "171  TCGA-XN-A8T5     0    0     0  With Tumor\n",
              "172  TCGA-YB-A89D     1    1     1  With Tumor\n",
              "173  TCGA-YH-A8SY     1    1     1  With Tumor\n",
              "174  TCGA-YY-A8LH     1    1     1  With Tumor\n",
              "175  TCGA-Z5-AAPL     0    0     0  With Tumor\n",
              "\n",
              "[176 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>PATIENT_ID</th>\n",
              "      <th>SMAD4</th>\n",
              "      <th>CDH7</th>\n",
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              "      <th>0</th>\n",
              "      <td>TCGA-2J-AAB1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>TCGA-2J-AAB4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>With Tumor</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>TCGA-2J-AAB6</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>TCGA-2J-AAB8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGA-2J-AAB9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>TCGA-XN-A8T5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>TCGA-YB-A89D</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>TCGA-YH-A8SY</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>TCGA-YY-A8LH</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>TCGA-Z5-AAPL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>176 rows × 5 columns</p>\n",
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              "    </style>\n",
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              "        google.colab.notebook.generateWithVariable('total_mut_per_patient');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total_mut_per_patient",
              "summary": "{\n  \"name\": \"total_mut_per_patient\",\n  \"rows\": 176,\n  \"fields\": [\n    {\n      \"column\": \"PATIENT_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 176,\n        \"samples\": [\n          \"TCGA-2L-AAQI\",\n          \"TCGA-F2-A44G\",\n          \"TCGA-IB-AAUP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMAD4\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH7\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH20\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DSS_STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"With Tumor\",\n          \"Tumor Free\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate and show the mutation frequency for each gene separated by survival outcome\n",
        "mut_freq = total_mut_per_patient.drop('PATIENT_ID', axis=1).groupby('DSS_STATUS').sum()/has_mut_or_cna.shape[1]*100\n",
        "print('Percent of All Patients With Mutations In Each Gene Based on Survival Status:')\n",
        "mut_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "AC3xx1QtkGFh",
        "outputId": "59bb87f0-7468-4b74-e03c-7558274a5027"
      },
      "id": "AC3xx1QtkGFh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percent of All Patients With Mutations In Each Gene Based on Survival Status:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SMAD4       CDH7      CDH20\n",
              "DSS_STATUS                                 \n",
              "Tumor Free  32.065217  30.978261  30.978261\n",
              "With Tumor  38.043478  36.413043  36.413043"
            ],
            "text/html": [
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              "      <th>Tumor Free</th>\n",
              "      <td>32.065217</td>\n",
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              "      <td>30.978261</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mut_freq",
              "summary": "{\n  \"name\": \"mut_freq\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"DSS_STATUS\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"With Tumor\",\n          \"Tumor Free\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMAD4\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 32.065217391304344,\n        \"max\": 38.04347826086957,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          38.04347826086957,\n          32.065217391304344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH7\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 30.978260869565215,\n        \"max\": 36.41304347826087,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          36.41304347826087,\n          30.978260869565215\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH20\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 30.978260869565215,\n        \"max\": 36.41304347826087,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          36.41304347826087,\n          30.978260869565215\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the mutation frequencies above, the mutation frequencies are much higher for samples that are tumor free for all of top 3 mutated genes in the Wnt pathway. This suggests that mutations in all three genes might be associated with an increase in survival outcome."
      ],
      "metadata": {
        "id": "Vsf9qUcxlJQE"
      },
      "id": "Vsf9qUcxlJQE"
    },
    {
      "cell_type": "markdown",
      "id": "16d952dd-9f73-4d46-a642-a88231ac91ec",
      "metadata": {
        "id": "16d952dd-9f73-4d46-a642-a88231ac91ec"
      },
      "source": [
        "#### e. How often is the pathway mutated overall in this cancer (how many patients have one or more genes mutated)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7bc1b45-2a56-44de-81f6-a788f3d7fed3",
      "metadata": {
        "id": "e7bc1b45-2a56-44de-81f6-a788f3d7fed3",
        "outputId": "6f51e831-9c53-4bde-865f-6d326605a9a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "138/184 patients have 1 or more genes mutated\n"
          ]
        }
      ],
      "source": [
        "# create a dataframe showing number of gene mutated for each patient\n",
        "num_mutated = wnt_mutations.groupby('Tumor_Sample_Barcode')['Hugo_Symbol'].size().reset_index()\n",
        "# rename the columns\n",
        "num_mutated.columns = ['sample', '# genes']\n",
        "# show the number of samples with 1+ gene mutated\n",
        "at_least1 = num_mutated[num_mutated['# genes'] >= 1].shape[0]\n",
        "# show results\n",
        "print(f'{at_least1}/184 patients have 1 or more genes mutated')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a52a56ca-6a0c-4177-a877-ff82793e347e",
      "metadata": {
        "id": "a52a56ca-6a0c-4177-a877-ff82793e347e"
      },
      "source": [
        "### 4. Gene Expression Analysis\n",
        "Pick a gene (possibly the same gene) for each of the gene expression analyses below. For each of the two types of (epi)genomic changes listed below, make a figure to visualize the association of the (epi)genomic alteration and gene expression. Then, interpret your figure: this includes stating how the (epi)genomic alteration affects this gene and what we can conclude about which subjects have oncogenic changes to this gene.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02cd839c-6a6e-4ab7-b59e-26322f9cc0c4",
      "metadata": {
        "id": "02cd839c-6a6e-4ab7-b59e-26322f9cc0c4"
      },
      "source": [
        "#### a. Association of expression and methylation at the locus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88f2a77e-f1fa-4a33-97ce-bea904049982",
      "metadata": {
        "id": "88f2a77e-f1fa-4a33-97ce-bea904049982",
        "outputId": "8642d75e-6d5f-42ed-c192-74173ec77945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sample expression methylation\n",
              "0    TCGA-2J-AAB1     0.6559    0.043253\n",
              "1    TCGA-2J-AAB4     -0.381    0.050801\n",
              "2    TCGA-2J-AAB6     -0.921       0.041\n",
              "3    TCGA-2J-AAB8    -0.5721    0.048676\n",
              "4    TCGA-2J-AAB9     0.3982    0.054787\n",
              "..            ...        ...         ...\n",
              "166  TCGA-XN-A8T5     0.1801    0.052656\n",
              "167  TCGA-YB-A89D      0.157    0.051189\n",
              "168  TCGA-YH-A8SY    -0.5816    0.039769\n",
              "169  TCGA-YY-A8LH     0.1962    0.065629\n",
              "170  TCGA-Z5-AAPL     0.2616    0.044798\n",
              "\n",
              "[171 rows x 3 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp_methyl",
              "summary": "{\n  \"name\": \"exp_methyl\",\n  \"rows\": 171,\n  \"fields\": [\n    {\n      \"column\": \"sample\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 171,\n        \"samples\": [\n          \"TCGA-HZ-A77P\",\n          \"TCGA-FB-AAPP\",\n          \"TCGA-FB-AAPS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"expression\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -3.27,\n        \"max\": 1.8566,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.3231,\n          1.2802,\n          0.587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"methylation\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0218829270239844,\n        \"max\": 0.0891017740050106,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.0593099044418791,\n          0.0535387983678112,\n          0.0558432512399354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# store the name of the gene\n",
        "gene = 'SMAD4'\n",
        "# create a dataframe containing the expression levels of a tumor\n",
        "exp_methyl = pd.DataFrame(columns=['sample',])\n",
        "# add the sample names\n",
        "exp_methyl['sample'] = all_patients\n",
        "\n",
        "# store the mRNA levels for gene\n",
        "gene_exp = mRNA_exp[mRNA_exp['Hugo_Symbol'] == gene].iloc[0, 2:].reset_index()\n",
        "# rename columns\n",
        "gene_exp.columns = ['sample', 'expression']\n",
        "# remove numbers at the end of sample names\n",
        "gene_exp['sample'] = gene_exp['sample'].str[:-3]\n",
        "# add the expression levels for the patients\n",
        "gene_exp = pd.merge(exp_methyl, gene_exp, on='sample', how='inner')\n",
        "\n",
        "# store the methylation levels for gene\n",
        "gene_methyl = methylation_levels[methylation_levels['NAME'] == gene].iloc[0, 2:].reset_index()\n",
        "# rename columns\n",
        "gene_methyl.columns = ['sample', 'methylation']\n",
        "# remove numbers at the end of sample names\n",
        "gene_methyl['sample'] = gene_methyl['sample'].str[:-3]\n",
        "# add the expression levels for the patients\n",
        "exp_methyl = pd.merge(gene_exp, gene_methyl, on='sample', how='inner')\n",
        "exp_methyl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc23e7ae-047b-4d32-bf65-cd563985d2c9",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Association of Expression and Methylation in SMAD4\",\"x\":0.5},\"height\":500},                        {\"responsive\": true}                    ).then(function(){\n",
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          "metadata": {}
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      ],
      "source": [
        "# create a scatter plot with line of best fit\n",
        "fig = px.scatter(exp_methyl, x = 'methylation', y = 'expression', trendline = 'ols')\n",
        "\n",
        "# add labels to figure\n",
        "fig.update_layout(\n",
        "    title = 'Figure 3. Association of Expression and Methylation in ' + gene,\n",
        "    title_x = 0.5,\n",
        "    xaxis_title = 'Methylation levels of ' + gene,\n",
        "    yaxis_title = 'mRNA Expression levels of levels of ' + gene,\n",
        "    height = 500)\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c624304-8a88-4ed8-bb91-cd273f62e462",
      "metadata": {
        "id": "0c624304-8a88-4ed8-bb91-cd273f62e462"
      },
      "source": [
        "Based on this scatter plot, the methylation levels of SMAD4 does not seem to affect SMAD4 expression levels."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41e73ebc-6a54-44d5-8406-2d7c2228d9eb",
      "metadata": {
        "id": "41e73ebc-6a54-44d5-8406-2d7c2228d9eb"
      },
      "source": [
        "#### b. Association of expression and copy number at the locus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df91057b-6cc1-470b-979a-c83f7866ef54",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sample expression CNA\n",
              "0    TCGA-2J-AAB1     0.6559   0\n",
              "1    TCGA-2J-AAB4     -0.381  -1\n",
              "2    TCGA-2J-AAB6     -0.921  -1\n",
              "3    TCGA-2J-AAB8    -0.5721  -2\n",
              "4    TCGA-2J-AAB9     0.3982   0\n",
              "..            ...        ...  ..\n",
              "165  TCGA-XN-A8T5     0.1801   0\n",
              "166  TCGA-YB-A89D      0.157  -1\n",
              "167  TCGA-YH-A8SY    -0.5816  -1\n",
              "168  TCGA-YY-A8LH     0.1962   1\n",
              "169  TCGA-Z5-AAPL     0.2616   0\n",
              "\n",
              "[170 rows x 3 columns]"
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              "      <th>0</th>\n",
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              "      <td>-0.921</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp_cna",
              "summary": "{\n  \"name\": \"exp_cna\",\n  \"rows\": 170,\n  \"fields\": [\n    {\n      \"column\": \"sample\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 170,\n        \"samples\": [\n          \"TCGA-IB-AAUV\",\n          \"TCGA-3A-A9IN\",\n          \"TCGA-IB-7897\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"expression\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -3.27,\n        \"max\": 1.8566,\n        \"num_unique_values\": 170,\n        \"samples\": [\n          1.0397,\n          1.7244,\n          1.3575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CNA\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"-2\",\n        \"max\": \"1\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"-1\",\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# add the expression levels for the patients\n",
        "exp_cna = pd.merge(gene_exp, SMAD4_cna, on='sample', how='inner')\n",
        "exp_cna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d314b175-074d-4913-a4f3-e69acc21a500",
      "metadata": {
        "id": "d314b175-074d-4913-a4f3-e69acc21a500",
        "outputId": "90a2c1f9-2095-423d-d59b-9410eeec9315",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
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Expression and Copy Number in SMAD4\",\"x\":0.5},\"height\":500},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# create a violin plot describing the mRNA expression levels of SMAD4\n",
        "fig = px.violin(exp_cna, x = 'CNA', y = 'expression', box=True)\n",
        "\n",
        "# add labels to figure\n",
        "fig.update_layout(\n",
        "    title = 'Figure 4. Expression and Copy Number in ' + gene,\n",
        "    title_x = 0.5,\n",
        "    xaxis_title = 'Copy Number of ' + gene,\n",
        "    yaxis_title = 'mRNA Expression levels of ' + gene,\n",
        "    height = 500)\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a281bf2c-9a63-45e4-b3d8-8b765ea72277",
      "metadata": {
        "id": "a281bf2c-9a63-45e4-b3d8-8b765ea72277",
        "outputId": "e4719121-d058-4f86-a4eb-f403fb371add",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sample expression log2 CNA\n",
              "0    TCGA-2J-AAB1     0.6559   -0.063\n",
              "1    TCGA-2J-AAB4     -0.381   -0.315\n",
              "2    TCGA-2J-AAB6     -0.921   -0.587\n",
              "3    TCGA-2J-AAB8    -0.5721    -0.35\n",
              "4    TCGA-2J-AAB9     0.3982   -0.094\n",
              "..            ...        ...      ...\n",
              "165  TCGA-XN-A8T5     0.1801   -0.076\n",
              "166  TCGA-YB-A89D      0.157   -0.215\n",
              "167  TCGA-YH-A8SY    -0.5816   -0.504\n",
              "168  TCGA-YY-A8LH     0.1962    0.109\n",
              "169  TCGA-Z5-AAPL     0.2616   -0.049\n",
              "\n",
              "[170 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b598e27-83f7-4a3c-9032-65ff2ea66a32\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>expression</th>\n",
              "      <th>log2 CNA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TCGA-2J-AAB1</td>\n",
              "      <td>0.6559</td>\n",
              "      <td>-0.063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGA-2J-AAB4</td>\n",
              "      <td>-0.381</td>\n",
              "      <td>-0.315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGA-2J-AAB6</td>\n",
              "      <td>-0.921</td>\n",
              "      <td>-0.587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGA-2J-AAB8</td>\n",
              "      <td>-0.5721</td>\n",
              "      <td>-0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGA-2J-AAB9</td>\n",
              "      <td>0.3982</td>\n",
              "      <td>-0.094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
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              "      <td>0.157</td>\n",
              "      <td>-0.215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>TCGA-YH-A8SY</td>\n",
              "      <td>-0.5816</td>\n",
              "      <td>-0.504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>TCGA-YY-A8LH</td>\n",
              "      <td>0.1962</td>\n",
              "      <td>0.109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>TCGA-Z5-AAPL</td>\n",
              "      <td>0.2616</td>\n",
              "      <td>-0.049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "exp_log2_cna",
              "summary": "{\n  \"name\": \"exp_log2_cna\",\n  \"rows\": 170,\n  \"fields\": [\n    {\n      \"column\": \"sample\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 170,\n        \"samples\": [\n          \"TCGA-IB-AAUV\",\n          \"TCGA-3A-A9IN\",\n          \"TCGA-IB-7897\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"expression\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -3.27,\n        \"max\": 1.8566,\n        \"num_unique_values\": 170,\n        \"samples\": [\n          1.0397,\n          1.7244,\n          1.3575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log2 CNA\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -1.29,\n        \"max\": 1.44,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          -0.027,\n          -0.552,\n          -0.008\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# location to file with the log2 copy number\n",
        "file_path = 'data_log2_cna.txt'\n",
        "# read in the file as a dataframe\n",
        "log2_cna = pd.read_table(file_path)\n",
        "# log2 CNA levels for gene\n",
        "gene_log2_cna = log2_cna[log2_cna['Hugo_Symbol'] == gene].iloc[0, 2:].reset_index()\n",
        "# rename columns\n",
        "gene_log2_cna.columns = ['sample', 'log2 CNA']\n",
        "# remove numbers at the end of sample names\n",
        "gene_log2_cna['sample'] = gene_log2_cna['sample'].str[:-3]\n",
        "# add the expression levels for the patients\n",
        "exp_log2_cna = pd.merge(gene_exp, gene_log2_cna, on='sample', how='inner')\n",
        "exp_log2_cna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b7f33f9-de23-4f77-b61c-bf415d5c74e3",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
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      "outputs": [
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      ],
      "source": [
        "# create a scatter plot with line of best fit\n",
        "fig = px.scatter(exp_log2_cna, x = 'log2 CNA', y = 'expression', trendline = 'ols')\n",
        "\n",
        "# add labels to figure\n",
        "fig.update_layout(\n",
        "    title = 'Figure 5. mRNA Expression and Log-Copy Number in ' + gene,\n",
        "    title_x = 0.5,\n",
        "    xaxis_title = 'Log2(Copy Number of ' + gene + ')',\n",
        "    yaxis_title = 'mRNA Expression levels of levels of ' + gene,\n",
        "    height = 500)\n",
        "\n",
        "# show figure\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2aae7530-0e43-48cc-9b43-0e479cd0fe09",
      "metadata": {
        "id": "2aae7530-0e43-48cc-9b43-0e479cd0fe09"
      },
      "source": [
        "Results show that while DNA methylation levels do not appear to directly influence SMAD4 mRNA expression (Figure 3), increasing copy number (amplification) does increase overall SMHD4 expression levels (Figures 4 and 5). This suggests that patients with an increase in copy number in this gene may be associated with oncogenic changes. Below are the patients that exhibit amplification of this gene."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e618fc22-6171-4df3-ae25-c835261ddb1f",
      "metadata": {
        "id": "e618fc22-6171-4df3-ae25-c835261ddb1f",
        "outputId": "7915617f-74fd-4dd8-aaa4-062f846e658d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sample expression CNA\n",
              "12   TCGA-2J-AABP      1.184   1\n",
              "22   TCGA-2L-AAQM      1.306   1\n",
              "30   TCGA-3A-A9IN     1.7244   1\n",
              "35   TCGA-3A-A9IV     1.8566   1\n",
              "54   TCGA-FB-AAPP     1.2802   1\n",
              "60   TCGA-FB-AAQ1      0.741   1\n",
              "168  TCGA-YY-A8LH     0.1962   1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98c18356-5e2d-4f4d-89d9-12f95bd27d92\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>expression</th>\n",
              "      <th>CNA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>TCGA-2J-AABP</td>\n",
              "      <td>1.184</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>TCGA-2L-AAQM</td>\n",
              "      <td>1.306</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>TCGA-3A-A9IN</td>\n",
              "      <td>1.7244</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>TCGA-3A-A9IV</td>\n",
              "      <td>1.8566</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>TCGA-FB-AAPP</td>\n",
              "      <td>1.2802</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>TCGA-FB-AAQ1</td>\n",
              "      <td>0.741</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>TCGA-YY-A8LH</td>\n",
              "      <td>0.1962</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# show the samples that exhibited amplification of this gene\n",
        "exp_cna[exp_cna['CNA'] > 0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ba8ae2c-cfd1-47be-bf7a-8679b0db4eea",
      "metadata": {
        "id": "7ba8ae2c-cfd1-47be-bf7a-8679b0db4eea"
      },
      "source": [
        "Note that changes in expression levels are low, suggesting that copy number alterations may not affect SMAD4 levels by much"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "043459a7-e4c9-40db-965f-16096a870e32",
      "metadata": {
        "id": "043459a7-e4c9-40db-965f-16096a870e32"
      },
      "source": [
        "### 5. Mutual exclusivity and co-occurrence analysis\n",
        " Analyze your top 3 most mutated genes from above. Make a visualization that allows you to compare mutual exclusivity or co-occurrence for all pairs of any two of these mutations."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a heatmap showing the number of mutations in each of the top 3 genes for each patient\n",
        "cols = ['PATIENT_ID', 'DSS_STATUS']\n",
        "fig = px.imshow(total_mut_per_patient.drop(columns=cols, axis=1))\n",
        "\n",
        "# add title, axis labels\n",
        "fig.update_layout(title = \"Figure 6. Mutation Frequencies of the Top 3 Mutated Genes Across All Patients\",\n",
        "                  title_x = 0.5,\n",
        "                  xaxis_title = 'Gene',\n",
        "                  yaxis_title = 'Patient',\n",
        "                  coloraxis_colorbar_title='mutation frequency')\n",
        "\n",
        "# move the x-axis labels to the top\n",
        "fig.update_xaxes(side=\"bottom\")\n",
        "\n",
        "# show heatmap\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rEWN06oqMGZP",
        "outputId": "d874a9cf-b14c-4702-9c83-29ad295d7041"
      },
      "id": "rEWN06oqMGZP",
      "execution_count": null,
      "outputs": [
        {
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frequency\"}}},\"margin\":{\"t\":60},\"title\":{\"text\":\"Figure 6. Mutation Frequencies of the Top 3 Mutated Genes Across All Patients\",\"x\":0.5}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('531b068a-ce65-4108-8610-becaa6750c21');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d52ccb01-48a8-46b8-8806-56d3de9eebb0",
      "metadata": {
        "id": "d52ccb01-48a8-46b8-8806-56d3de9eebb0"
      },
      "source": [
        "The above heatmap (Figure 6) shows the number of mutations that each patient had in each gene. Each row is one patient and each column represents the number of mutations in one of the top 3 mutated gense. You can see that the mutation frequencies across all 3 mutated genes are similar as they have similar banding patterns. In other words, patients with a mutation in SMAD4 seem to also have a mutation in CDH7 and CDH20, suggesting that mutations in all three genes are co-occurent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac699303-2297-4d1c-a754-080be8452b74",
      "metadata": {
        "id": "ac699303-2297-4d1c-a754-080be8452b74"
      },
      "source": [
        "### 6. Make one other plot\n",
        "Make one other plot to learn about the effect of this pathway on this cancer, using any of the data you have about the cancer cohort. Meaning, a different from the ones already required, using coding."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd653d6a-6782-459d-abd9-0a79ac6fbebf",
      "metadata": {
        "id": "dd653d6a-6782-459d-abd9-0a79ac6fbebf"
      },
      "source": [
        "#### a. Form a hypothesis of something you expect to see in this cancer data."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From part 3C, it was found that mutations in the top 3 mutated genes in the Wnt pathway were inactivating and more likely to be associated with tumor suppressor activity in patients with pancreatic adenocarcinoma. Therefore, if we compared the gene expression of patients without and without the tumor of these top 3 mutated genes in the Wnt pathway, we might expect that tumor patients may have lower expression levels for these genes compared to tumor free patients."
      ],
      "metadata": {
        "id": "bt38lNmf1XNG"
      },
      "id": "bt38lNmf1XNG"
    },
    {
      "cell_type": "markdown",
      "id": "91515799-e590-470b-8e7d-a77cfca7daf5",
      "metadata": {
        "id": "91515799-e590-470b-8e7d-a77cfca7daf5"
      },
      "source": [
        "#### b. Describe what kind of plot you choose and how it will help assess your hypothesis. What do you expect to see?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To assess this hypothesis, I could create a figure containing box plots describing the mRNA expression levels in tumor-free patients vs tumor patients for each of the top 3 mutated genes in the Wnt pathway.\n",
        "\n",
        "Components of the figure:\n",
        "\n",
        "* *X-axis:* Each of the top 3 mutated genes, separated by survival outcome (tumor vs tumor-free)\n",
        "\n",
        "* *Y-axis:* mRNA expression levels for each group of patients (tumor vs tumor-free)\n",
        "\n",
        "* *Colors:* Each represents group of patients based on survival outcome (tumor vs tumor-free)\n",
        "\n",
        "*Rationale:* This figure would allow us to compare the expression levels of tumor vs tumor-free patients. If these mutations are in fact associated with tumor suppressor activity, then we expect that for all the genes, the average expression levels in patients with pancreatic adenocarcinoma would be lower for all 3 genes."
      ],
      "metadata": {
        "id": "T8e2bW9B2j3h"
      },
      "id": "T8e2bW9B2j3h"
    },
    {
      "cell_type": "markdown",
      "id": "ce4998dc-bf2a-426b-b6d9-defc0054f600",
      "metadata": {
        "id": "ce4998dc-bf2a-426b-b6d9-defc0054f600"
      },
      "source": [
        "#### c. Provide code to make the plot. The code must work and must be explained so someone else can understand how it works"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a dataframe containing the mRNA expression levels of the top 3 mutated genes in the Wnt pathway\n",
        "top_genes_exp = mRNA_exp[mRNA_exp['Hugo_Symbol'].isin(top_genes)].drop('Entrez_Gene_Id', axis=1)\n",
        "# rearrange the dataframe so that the samples are the rows and the columns are the genes\n",
        "top_genes_exp = top_genes_exp.transpose().reset_index()\n",
        "# rename columns\n",
        "top_genes_exp.columns = ['PATIENT_ID'] + top_genes_exp.iloc[0, 1:].tolist()\n",
        "# remove the first row containing the names of the genes\n",
        "top_genes_exp.drop(0, inplace=True)\n",
        "# remove the barcode for the samples to get just the patient ids\n",
        "top_genes_exp['PATIENT_ID'] = top_genes_exp['PATIENT_ID'].str[:-3]\n",
        "# add the patient survival outcomes\n",
        "top_genes_exp = pd.merge(top_genes_exp, survival_outcome, on='PATIENT_ID', how='inner')\n",
        "top_genes_exp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "sMVPHbeh5DsE",
        "outputId": "7cba5e8c-3cc5-4cf6-ab0e-e000433edef4"
      },
      "id": "sMVPHbeh5DsE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PATIENT_ID   CDH20    CDH7   SMAD4  DSS_STATUS\n",
              "0    TCGA-2J-AAB1   0.397 -1.2063  0.6559  Tumor Free\n",
              "1    TCGA-2J-AAB4 -1.2635 -1.2063  -0.381  With Tumor\n",
              "2    TCGA-2J-AAB6 -1.7781 -1.2063  -0.921  Tumor Free\n",
              "3    TCGA-2J-AAB8 -0.8073 -1.2063 -0.5721  With Tumor\n",
              "4    TCGA-2J-AAB9 -0.7111 -1.2063  0.3982  Tumor Free\n",
              "..            ...     ...     ...     ...         ...\n",
              "166  TCGA-XN-A8T5 -1.0934 -1.2063  0.1801  With Tumor\n",
              "167  TCGA-YB-A89D  1.2081 -0.3563   0.157  With Tumor\n",
              "168  TCGA-YH-A8SY -0.0132 -1.2063 -0.5816  With Tumor\n",
              "169  TCGA-YY-A8LH -0.8519 -1.2063  0.1962  With Tumor\n",
              "170  TCGA-Z5-AAPL -1.7781 -1.2063  0.2616  With Tumor\n",
              "\n",
              "[171 rows x 5 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PATIENT_ID</th>\n",
              "      <th>CDH20</th>\n",
              "      <th>CDH7</th>\n",
              "      <th>SMAD4</th>\n",
              "      <th>DSS_STATUS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TCGA-2J-AAB1</td>\n",
              "      <td>0.397</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>0.6559</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGA-2J-AAB4</td>\n",
              "      <td>-1.2635</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>-0.381</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGA-2J-AAB6</td>\n",
              "      <td>-1.7781</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>-0.921</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGA-2J-AAB8</td>\n",
              "      <td>-0.8073</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>-0.5721</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGA-2J-AAB9</td>\n",
              "      <td>-0.7111</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>0.3982</td>\n",
              "      <td>Tumor Free</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>TCGA-XN-A8T5</td>\n",
              "      <td>-1.0934</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>0.1801</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>TCGA-YB-A89D</td>\n",
              "      <td>1.2081</td>\n",
              "      <td>-0.3563</td>\n",
              "      <td>0.157</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>TCGA-YH-A8SY</td>\n",
              "      <td>-0.0132</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>-0.5816</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>TCGA-YY-A8LH</td>\n",
              "      <td>-0.8519</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>0.1962</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>TCGA-Z5-AAPL</td>\n",
              "      <td>-1.7781</td>\n",
              "      <td>-1.2063</td>\n",
              "      <td>0.2616</td>\n",
              "      <td>With Tumor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>171 rows × 5 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_genes_exp",
              "summary": "{\n  \"name\": \"top_genes_exp\",\n  \"rows\": 171,\n  \"fields\": [\n    {\n      \"column\": \"PATIENT_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 171,\n        \"samples\": [\n          \"TCGA-HZ-A77P\",\n          \"TCGA-FB-AAPP\",\n          \"TCGA-FB-AAPS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH20\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -1.7781,\n        \"max\": 4.5217,\n        \"num_unique_values\": 162,\n        \"samples\": [\n          -1.0934,\n          -1.1358,\n          1.2999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDH7\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -1.2063,\n        \"max\": 4.288,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          -0.3888,\n          -0.0119,\n          -0.3079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMAD4\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -3.27,\n        \"max\": 1.8566,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.3231,\n          1.2802,\n          0.587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DSS_STATUS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"With Tumor\",\n          \"Tumor Free\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# combine the mRNA expression levels for each gene into one column and have a column with the gene, that expression level is from\n",
        "compare_gene_exp = top_genes_exp.melt(id_vars=['PATIENT_ID', 'DSS_STATUS'], var_name='Gene', value_name='mRNA Expression Levels')\n",
        "\n",
        "# create a figure showing the violin plots comparing mRNA expression for each gene and survival outcome\n",
        "fig = px.box(compare_gene_exp.drop('PATIENT_ID', axis=1),\n",
        "             x='Gene',\n",
        "             y='mRNA Expression Levels',\n",
        "             color='DSS_STATUS')\n",
        "# add title\n",
        "fig.update_layout(title='Figure 7. mRNA Expression Levels for the Top 3 Mutated Genes Based on Survival Outcome',\n",
        "                  title_x=0.5,\n",
        "                  legend_title_text = 'Survival Outcome')\n",
        "# show plot\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "EzoyS1Hc9Rww",
        "outputId": "bde8619a-940b-49b7-a039-f09507df7ba8"
      },
      "id": "EzoyS1Hc9Rww",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "#### d. Interpret the plot: how does the plot allow you to assess your hypothesis? What do you conclude by looking at this."
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        "Looking at Figure 7, it seems that mRNA expression levels were slightly lower for patients with pancreatic adenocarcinoma for all the top 3 mutated genes in the Wnt pathway but not signficantly lower. This suggests that contrary to initial hypothesis, these genes do not seem to be associated with tumor suppressor activity. However, this could be due to the few number of point mutations across all patients in all these genes (refer to part 3C)."
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        "### 7. Another Research Article\n",
        "Find one other research article related to this type of cancer that refers to either: the activity of this pathway; or the genes/mutations in the pathway; or a drug and how it can impact that pathway in that cancer.\n",
        "\n",
        "Research article: https://www.nature.com/articles/nature14169#Sec13\n",
        "#### a. Summarize article\n",
        "Describe the:\n",
        "* knowledge gap\n",
        "* hypothesis\n",
        "* experimental design of the paper (what are the observations and how many observations; and what are the variables and approximately how many)\n"
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      "source": [
        "***Knowledge Gap***\n",
        "\n",
        "> PDAC exhibits a diverse mutational landscape, with mutations in key genes such as KRAS, TP53, SMAD4, and CDKN2A being common, but with a long tail of infrequently mutated genes contributing to significant intertumoral heterogeneity. This heterogeneity makes it challenging to develop effective therapies, as treatments that target specific mutations may only benefit a subset of patients.\n",
        "\n",
        "***Hypothesis***\n",
        "\n",
        "> Whole genome sequencing and copy number variation can help to characterize the different types of pancreatic cancer and idenitfy genetic biomarkers associated with therapeutic response, improving therapeutic strategies and patient outcomes.\n",
        "\n",
        " ***Experimental Design***\n",
        "\n",
        "> * Observations: 100 PDAC tumor samples\n",
        "> * For each tumor sample, they analyzed the following genetic alterations across 100 tumor samples:\n",
        ">   * Somatic Mutations: 857,971 somatic point mutations and small insertions and deletions.\n",
        ">   * Non-silent Mutations: 7,888 mutations in 5,424 genes.\n",
        ">   * Structural Variants: 11,868 somatic structural variants."
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      "source": [
        "#### b. Summarize how the findings in the article relates to your findings above about the pathway"
      ]
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        "> This paper relates to the Wnt pathway as it also lists SMAD4, one of the top mutated genes that I found in my analysis, as one of the top mutated genes in pancreatic adenocarcinoma. The researchers found that combining structural variation events with deleterious point mutations increased the frequency of inactivating SMAD4. This matches with the results from part 3C showing that a high percentage of all patients exhibited a deleterious point mutation and copy number alterion in SMAD4, suggesting that mutations in this gene are inactivating. However, there was a greater number of copy number variations in SMAD4 compared to point mutations, contributing to higher overall inactivating mutation frequencies.\n",
        ">\n",
        "> Additionally, they found that SMAD4 mutations occur at comparable frequencies across their cohort. This matches with findings from part 3D which showed a similar percentage of mutations in SMAD4 in patients with and without the tumor. Both of these findings suggest that SMAD4 mutations are not specific to a particular subtype or group of patients."
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      "source": [
        "#### c. Include the two most relevant figures from the paper!\n",
        "For each figure:\n",
        "* explain what are the axes (horizontal and vertical)\n",
        "* what the points, bars, colors mean (depending on the plot. Please see me if you are not sure which parts to explain)\n",
        "* Copy over one sentence (quote the sentence) showing a conclusions they made based on this figure and explain how the figure supports the sentence."
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "#### Figure 1"
      ],
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      "source": [
        "![Figure 1](https://drive.google.com/uc?id=1fnd5Z-M96Hh35bEJ0TpdT-qirgkMraTd)"
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        "Top panel:\n",
        "* *X-axis:* Each bar represents one tumor samples\n",
        "* *Y-axis:* The height of each bar represents the total number of non-silent single nucleotide variants and small insertions or deletions in that tumor sample\n",
        "\n",
        "Central matrix:\n",
        "* *X-axis:* Each column represents one tumor sample\n",
        "* *Y-axis:* Each row represents one gene\n",
        "* *Colors:*\n",
        "  * Blue – Non-silent mutations\n",
        "  * Copy Number Changes:\n",
        "    * Red – Amplification (> 5 copies)\n",
        "    * Green – Loss\n",
        "  * Yellow – Structural Variants\n",
        "* *NOTES:* Asterisks (*) indicate pathogenic germline variants. The\n",
        "histogram on the left shows the number of each alteration in each gene.\n",
        "\n",
        "**Conclusion from the paper:** \"Chromothripsis is linked to TP53 mutations in medullobastoma and acute myeloid leukaemia and here, 10/13 chromothriptic tumours had a TP53 mutation, 5 of which were bi-allelic (Fig. 1).\" [[6]](https://www.nature.com/articles/nature14169#Sec13)\n",
        "\n",
        "**How the figure supports this conclusion:** They classified samples in the locally rearranged subtype to have a genetic alteration that affects a localized or specific region of the genome. Chromothriptic tumors involves numerous chromosomal alterations that are primarily localized to one or a few chromosomes and result. From the central matrix of the figure, ten samples (shown as dark green squares) had a the large amount of genetic alterations and loss present in those samples. Examining all tumors in this subtype more closely, they found that these samples were Chromothriptic tumors out of the total of 13 Chromothriptic tumors in this subtype."
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      "source": [
        "#### Figure 2b"
      ]
    },
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      "source": [
        "![Figure 2b](https://drive.google.com/uc?id=12wUsg7ffRCCLgqfEUOBA9VwRcPobR1Vy)"
      ],
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      "source": [
        "Top panel:\n",
        "* *X-axis:* Each bar represents one tumor sample\n",
        "* *Y-axis:* The height of each bar represents the number of mutations in the BCRA gene per megabase (Mb)\n",
        "* *NOTE:* Bars to the left of the green bar are samples with the greatest BRCA mutations frequencies.\n",
        "\n",
        "Bottom panel:\n",
        "* *X-axis:* Each column represents one tumor sample.\n",
        "* *Y-axis:* Each row represents the one gene in the BCRA pathway\n",
        "* *Colors:* Each color represents the type of mutation\n",
        "* *NOTES:*\n",
        "  * Tumor samples have been ordered from left being the greatest number of BRCA mutations Mb to the right with fewest number of BRCA mutations per Mb.\n",
        "  * The dagger (†) symbol indicates predicted only as possibly damaging by Polyphen2, a computational tool used to predict the possible impact of amino acid substitutions on the structure and function of proteins.\n",
        "\n",
        "**Conclusion from paper:** \"Unstable tumours are associated with a high BRCA mutation signature and deleterious mutations in BRCA pathway genes.\" [[6]](https://www.nature.com/articles/nature14169#Sec13)\n",
        "\n",
        "**How the figure supports this conclusion:** The bottom panel contains a row that indicates whether that patient had an unstable tumor. All unstable tumors were above the green bar in the top panel, which indicates the samples with the highest frequency of BCRA mutations. This suggests that unstable tumors exhibit high rates of BRCA mutations. Additionally, in the bottom panel, most of patients with unstable tumors exhibit non-silent mutations and loss of heterozygosity (LOH) mutations (shown in light blue). LOH mutations occur when one of the two alleles at a particular genomic region is lost or becomes inactivated. Both types of mutations result in a loss of function of the gene."
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      "source": [
        "### References\n",
        "[1] “USCS Data Visualizations.” Available: https://gis.cdc.gov/grasp/USCS/DataViz.html\n",
        "\n",
        "[2] “What Is Pancreatic Cancer? | Types of Pancreatic Cancer.” Available: https://www.cancer.org/cancer/types/pancreatic-cancer/about/what-is-pancreatic-cancer.html\n",
        "\n",
        "[3] T. Zhan, N. Rindtorff, and M. Boutros, “Wnt Signaling in Cancer,” *Oncogene, vol. 36, no. 11, pp. 1461–1473, Mar. 2017, doi: 10.1038/onc.2016.304. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357762/\n",
        "\n",
        "[4] “cBioPortal for Cancer Genomics.” Available: https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas_2018\n",
        "\n",
        "[5] “Gene Set - Wnt signaling pathway.” Available: https://maayanlab.cloud/Harmonizome/gene_set/Wnt+signaling+pathway/PANTHER+Pathways\n",
        "\n",
        "[6] N. Waddell *et al.*, “Whole Genomes Redefine the Mutational Landscape of Pancreatic Cancer,” *Nature*, vol. 518, no. 7540, pp. 495–501, Feb. 2015, doi: 10.1038/nature14169. Available: https://www.nature.com/articles/nature14169"
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